Manufacturing design and process analysis and simulation system

ABSTRACT

A process simulation system that simulates the operation of the manufacturing and measurement systems used to produce and measure the articles being analyzed relative to engineering design targets, engineering design tolerances, producibility and/or quality. In one embodiment, the user is able to assess, without risk or production cost while accelerating speed-to-market, the effect of contemplated changes (i.) to engineering design targets, (ii.) to engineering design tolerances, (iii.) to tooling, (iv.) to part pre-process dimensions and (v.) to the measurement system—on manufactured part dimensions, producibility and quality (i.) without modifying tooling, (ii.) without changing part pre-process dimensions, (iii.) without producing new parts, (iv.) without measuring article characteristics on the new parts and (v.) without changing the measurement system. The simulation functionalities, according to embodiments of the present invention, enable the user to verify whether or not the contemplated changes will have the desired effect without incurring the time and expense involved in actually making the changes, producing parts, measuring part characteristics, changing the measurement system and then determining whether the changes accomplished the desired objectives.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 10/752,843, which is a continuation of U.S. patentapplication Ser. No. 10/357,690 filed Feb. 4, 2003 and entitled“Manufacturing Design and Process Analysis System,” which is acontinuation-in-part application of co-pending and commonly-owned U.S.patent application Ser. No. 10/067,704 filed Feb. 4, 2002 and entitled“Manufacturing Design and Process Analysis System,” both of which areincorporated herein by reference for all purposes. The presentapplication also claims priority from U.S. Provisional Application Ser.No. 60/491,148 filed Jul. 30, 2003 and entitled “Manufacturing Designand Process Analysis and Simulation System.”

FIELD OF THE INVENTION

The present invention relates to manufacturing, design, tooling, andprocess engineering and, in one embodiment, to methods, apparatuses andsystems facilitating the design, tooling, production and/or measurementtasks associated with manufacturing and other processes. In oneembodiment, the present invention relates to decision-making and logicstructures, implemented in a computer software application, thatfacilitate all phases of the design, development, tooling,pre-production, qualification, certification, and production process ofany part or other article that is produced to specification.

BACKGROUND OF THE INVENTION

The world of manufacturing, including process engineering, has beenunder continuous and accelerating pressure to improve quality and reducecosts. This trend shows signs of further accelerating rather thandecelerating. From a manufacturing perspective, quality refers toproducing parts that 1.) are close to or at engineering design targets,and 2.) exhibit minimal variation. The realm of design engineering hasalso been under continuous pressure to improve quality and reduce costs.Design engineering must create nominal design targets and establishtolerance limits where it is possible for manufacturing to produce partsthat are 1.) on target and 2.) that fall within the design tolerancelimits. In-other-words, engineers are tasked not only with designingarticles to meet form, fit and function, but with designing them forproducibility.

In any manufacturing or other process, there are five fundamentalelements (see FIG. 1): 1) the process (A) that makes the product,provides the service or produces the result(s); 2) Inputs into theprocess (B); 3) Output from the process (C); 4) Process controlvariables adjusted to influence the process output (D); and, 5)uncontrolled process variables that influence the process (E) (e.g.,either uncontrollable variables or variables that are left uncontrolledbecause of time, cost or other considerations, collectively referred toas “noise.”).

The traditional approach to producing articles, such as parts or othercomponents, that meet design specifications is a logical one based on asearch for causation. This approach is based on the principle that,control over the variables that influence a process yields control overthe output of that process. In-other-words, if one can control thecause, then one can also control the effect. FIG. 2 illustrates thisprior art principle, where an attempt is made to determine therelationships, linkages, or correlations between the control variablesand the characteristics of the output (e.g., manufactured parts).Unfortunately, many manufacturing processes act like a black box. It canbe difficult in some of these cases to determine the relationshipbetween the process control variables and the resulting articlecharacteristic values. Furthermore, time and economic constraints canmake such a determination impractical even when this might betechnically possible.

Plastic injection molding is an example of this situation. With at least22 control variables, even when these control settings have only twolevels each (a high and a low temperature, a high and a low pressure,etc.), there are nevertheless over 4 million possible combinations.Indeed, there are billions of possible combinations when three levels(high, medium and low settings) are considered. Furthermore, changes toprocess variables may have varying effects on the resulting articlecharacteristics; for example, increasing a pressure setting can increasea first article characteristic, decrease a second, and not affect athird. Simple interactions, complex interactions and non-linearitiescomplicate the situation further. In addition, there are usuallymultiple mold cavities in a single mold. Finally, there are numerousarticle characteristics (dimensional, performance, or otherrequirements) that must be met. In light of the preceding, it is oftenextremely difficult to establish the combination of factors from thelarge number of part design targets, part tolerance limits, mold designcharacteristics and injection molding press settings that producesacceptable articles.

Some progress has been made in this regard. Design of Experiments (DOE)methodology greatly reduces the number of experiments that must beconducted to understand the impact of a selected subset of controlvariables on the resulting output of a process. Unfortunately, evenafter performing a designed experiment, there are still a large numberof control variables that can affect the resulting articles. In anyevent, extensive measurement of produced parts is still conducted byboth the supplier and the OEM customer to ensure that acceptablearticles are produced.

In addition, there are two main paths to achieving improvedmanufacturing quality. The first is to measure the parts after they areproduced and then compare the parts to specification requirements(design targets and tolerances). This is an “on-line” process utilizingfeedback. The parts are usually measured, to some extent, by both theproducer and the customer (OEM, first tier manufacturer, second tiermanufacturer, etc.). Measuring the parts, recording and analyzing thedata, and reporting the results, however, is a very expensive andresource consuming process.

In their efforts to improve quality, many manufacturers have begun touse the techniques of Statistical Process Control (SPC) and ProcessCapability studies. Indeed, many customers require their suppliers toperform SPC or other equivalent measurement, recording, analysis andreporting procedures. According to this technique, samples are takenfrom the production line, measured and then analyzed to see if anyabnormal (not normally distributed) patterns or data points emerge. Ifsuch abnormal data points are detected, the process is considered“out-of-control” (i.e., failing to yield a consistent predictableoutput) and production is immediately stopped to fix the process. Themeasurement data from manufactured parts is analyzed using sophisticatedSPC statistical methods and charting tools embodied in specializedcomputer programs. Since most parts have many different dimensions,measurement and SPC analysis have usually been applied to a large numberof part dimensions for each part, increasing the time and expenseassociated with production. However, SPC is far less expensive in thelong run than shipping unacceptable parts and/or having to sortacceptable parts from unacceptable parts.

It has also been difficult for manufacturers (and their customers) todetermine 1.) what percentage of the dimensions should be monitoredusing SPC and 2.) which dimensions should be measured if the full set ofdimensions is not monitored. Usually, most, if not all, of the“critical” dimensions called out by the design engineer are measured andanalyzed using SPC techniques. However, economic constraints can resultin fewer than the desired number of dimensions being measured andanalyzed. Guesswork is then frequently involved as to which dimensionsto select for SPC or other analysis.

A second path to improving manufacturing quality is by reducing thenatural variation in the manufactured articles. The accuracy ofmaintaining the process control factors can be improved and/or the“noise” factors can be eliminated or minimized. This is an “off-line”process improvement using feed-forward. Reducing natural variation isalso an expensive proposition since many relatively small common causesof variation are present. The degree to which the natural variation inthe parts produced must be reduced is usually determined throughexpensive process capability studies, usually conducted on each“critical” dimension.

In light of the foregoing, a need in the art exists for methods,apparatuses and systems facilitating design and manufacturing processesand, more particularly, addressing the problems discussed above. Forexample, a need in the art exists for methods and systems that allow forreductions in time and cost associated with the measurement, recording,analysis and reporting processes discussed above in connection with, forexample, SPC studies, Process Capability studies, shipping inspectionand receiving inspection. A need in the art exists for methods todetermine how to adjust inputs to a process in order to achieve thedesired outputs. A need in the art also exists for methods and systemsfacilitating a determination of how many article characteristics (e.g.,dimensions, performance measures, etc.) should be measured for a givenprocess. Lastly, a need in the art exists for methods and systems thatenable an assessment of which article characteristics should be measuredfor a given process. As discussed in more detail below, embodiments ofthe present invention substantially fulfill these needs.

SUMMARY OF THE INVENTION

The present invention provides methods, apparatuses and systems thatfacilitate the design, production and/or measurement tasks associatedwith manufacturing and other processes. In one embodiment, the presentinvention relates to decision-making and logic structures, implementedin a computer software application, facilitating all phases of thedesign, development, tooling, pre-production, qualification,certification, and production process of any part or other article thatis produced to specification. In one embodiment, the present inventionprovides knowledge of how the multiple characteristics of a givenprocess output are related to each other, to specification limits and topre-process inputs. This knowledge facilitates a reduction inmeasurement, analysis and reporting costs both prior to and duringproduction. In various implementations, the present invention allows forone or more of the following: It determines the changes needed topre-process inputs in order to achieve production at design targets. Itprovides a prioritized order for relaxing design tolerances. It assessesthe feasibility of producing parts that meet specification limits. Itassesses the trade-off between performance and producibility andprovides design targets that improve producibility. It provides adetermination of when process variability needs reduction. Itfacilitates material comparison and selection. It provides processengineers and operators with improved operating guidelines.

The present invention uses analytical techniques to accomplish thepreceding objectives and advantages. As discussed below, graphicaltechniques, in one embodiment, can optionally be used in addition to, orin place of, analytical techniques. Graphical techniques, including butnot limited to charts, graphs, and plots, can also be used to displayanalysis results. The present invention employs powerful statisticalmethodologies that, in one embodiment, allow for a determination ofwhich and how many article characteristics should be measured,potentially reducing the cost and resource expenditure associated withmeasurement, recording, analysis and reporting. Embodiments of thepresent invention also assist design engineers in designing articles forproducibility. Embodiments of the present invention can also beconfigured to provide critical information necessary for designengineers and tooling engineers to modify design requirements forprocess inputs in order to make it possible for manufacturing to hitdesign targets and stay within specification tolerance limits.Embodiments of the present invention can also be employed to identify,using a systems engineering approach, which article characteristics havethe most restrictive targets and specification tolerance limits. Suchinformation, for instance, allows for an evaluation of whether or nottolerances should be increased and, if so, which tolerances and on whicharticle characteristic. The present invention can also be employed toreduce the cost of performing process capability studies by reducing, insome cases dramatically so, the number of process capability studiesthat must be conducted. These and other aspects of the present inventionwill be become apparent from the following description of preferredembodiments of the present invention.

In one embodiment, the present invention provides methods, apparatusesand systems that facilitate understanding and analysis of how therelationship between predicted article characteristics and one or morepredictor article characteristics reflects the ability of a process toachieve a desired objective (e.g., the production of parts at targetspecification and/or within specification tolerances). In oneembodiment, the present invention provides a process analysis systemthat generates a set of graphs and/or tables (such as Constraint Tables,Offset Tables and Relaxation Tables) that enable the user to understandand analyze the relationship among article characteristics to makepowerful and informed decisions as to potential design or tooling, orpre-process dimensional or process changes. As discussed in more detailbelow, the relationship between the predictor characteristic and one ormore given predicted article characteristics can be generallycategorized into one of three possible situations: 1) where it ispossible to produce an article outside of specification limits as to thepredicted article characteristic; 2) where the predicted articlecharacteristic is robust and will always be inside of specificationlimits; and 3) where the predicted article characteristic constrains thepredictor article characteristic. In one embodiment, the presentinvention provides a methodology for analysis of the possiblerelationships between the predictor characteristic and the remainingarticle characteristics in order to determine, for example, whichpredicted article characteristics can be safely ignored, which predictedarticle characteristics will constrain the operating range or windowrelative to the predictor characteristic, and which predicted articlecharacteristics could result in producing articles outside of designspecification limits. With such a categorization of predicted articlecharacteristics, users (such as design engineers, tooling engineers,process engineers, inspectors and the like) are then in position to makedecisions as to how to treat each predicted article characteristic. Forexample, in the case where it is possible to produce defects, the usermay decide to relax specification tolerances, and/or modify pre-processinputs, and/or constrain process variables. In addition, if a givenpredicted article characteristic is robust (within specification) forall possible values (at least within design specification limits) of thepredictor article characteristic(s), the predicted articlecharacteristic can be ignored, for example, during post-processing taskssuch as part measurement and analysis associated with pre-qualification,qualification, certification and production activities. The presentinvention also facilitates analysis of the impact of predicted articlecharacteristics that constrain the allowable range for the predictorarticle characteristic(s). As discussed in more detail below, thepresent invention allows a user to consider the impact of a constrainingpredicted article characteristic, and degree of constraint, on theproducibility of the article as called out in a given designspecification, and also allows for an assessment of the suitability of agiven process to generate output that meets designspecifications/requirements. As discussed in more detail below,embodiments of the present invention are operative to generate graphs,tables and charts, such as Scatter Charts, Constraint Tables, OffsetTables and Relaxation Tables, that support the analytical anddecision-making tasks associated with manufacturing and other processes.

In one embodiment, the present invention further provides a design andprocess analysis system that simulates the operation of themanufacturing system used to produce the articles being analyzed and themeasurement system used to measure the article characteristics,including the manufacturing process, the measurement process, tooling,pre-process part dimensions, material responses and natural variation.In one embodiment, the user is able to assess the effect of contemplatedchanges—(i.) to engineering design targets, (ii.) to engineering designtolerances, (iii.) to tooling, and (iv.) to part pre-processdimensions—on manufactured part dimensions (i.) without modifyingtooling, (ii.) without changing part pre-process dimensions, (iii.)without producing new parts and (iv.) without measuring articlecharacteristics on the new parts. The simulation functionality,according to embodiments of the present invention, enables the user toverify whether or not the contemplated changes to design target, designtolerances, tooling and part pre-process dimensions will have thedesired effect. This simulation and verification is done withoutincurring the time and expense involved in actually making the changes,producing parts, measuring part characteristics and then determiningwhether the changes accomplished the desired objectives. In oneembodiment, the user inputs (i.) correction factors for tooling, and/or(ii.) correction factors for part pre-process dimensions, and/or (iii.)revised values for design targets, and/or (iv.) revised values fordesign tolerances. The process simulation and analysis system thensimulates the manufacturing process operating with corrected toolingand/or corrected pre-process part dimensions, creating a new, simulatedset of part dimensional data. This new set of dimensional data alongwith revised(if any) engineering design targets and tolerances is thenanalyzed using the process analysis system to verify that the desiredresults were obtained from the changes.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a process flow diagram generally applicable to manufacturingand other processes.

FIG. 2 is a process flow diagram illustrating a concept associated withapplication of prior art process control techniques to manufacturingprocesses.

FIG. 3 is a process flow diagram illustrating a concept associated withthe present invention as applied to manufacturing processes.

FIG. 4 is a scatter chart setting forth a regression model and thetarget intersection between two article characteristics.

FIG. 5 is a scatter chart modeling the effect a change in processcontrol settings has on process output.

FIG. 6 is a scatter chart illustrating the effect of changing processinputs.

FIG. 7 is a scatter chart illustrating the combined effect of changingprocess control settings and process inputs.

FIG. 8 is a scatter chart including prediction intervals associated witha regression model.

FIG. 9 provides a graphical user interface facilitating the input ofarticle characteristic data used in connection with an embodiment of thepresent invention.

FIG. 9A provides a graphical user interface facilitating the input ofarticle characteristic data and allowing the user to select variousanalysis options used in connection with another embodiment of thepresent invention.

FIG. 10 is a scatter chart illustrating a simple linear regression modelfor two article characteristics.

FIG. 11 is a functional block diagram illustrating an embodiment of acomputer hardware system suitable for use in connection with the presentinvention.

FIG. 12 is a scatter chart illustrating the concepts associated with anembodiment of the present invention.

FIG. 13 is a scatter chart including a linear regression model,prediction intervals, a target intersection, and upper and lowerspecification limits.

FIG. 13A is a scatter chart including a linear regression model,prediction intervals, a target intersection, and upper and lowerspecification limits, where the upper range of the articlecharacteristic data has been constrained.

FIG. 13B is a scatter chart including a linear regression model,prediction intervals, a target intersection, and upper and lowerspecification limits where the lower range of the article characteristicdata has been constrained.

FIG. 13C is a scatter chart including a linear regression model,prediction intervals, a target intersection, and upper and lowerspecification limits, where both the upper and lower ranges of thearticle characteristic data have been constrained.

FIG. 13D is a flow chart illustrating a method, according to anembodiment of the present invention, directed to revising Pmax and Pminfor each predicted characteristic when the data is constrained.

FIG. 14 is a scatter chart illustrating the determination of theallowable operating range and the operating target value for a predictorcharacteristic.

FIG. 15 illustrates a system architecture according to an embodiment ofthe present invention.

FIG. 16 is a flow chart providing a method according to an embodiment ofthe present invention.

FIG. 17 is a flow chart illustrating a method associated with displayinga regression model and associated analysis elements to a user.

FIG. 18 is a flow chart setting forth a method allowing for selection ofa predictor characteristic.

FIG. 19 is a spreadsheet table including a set of article characteristicvalues as to a plurality of article characteristics, correlationcoefficients and a value indicating the predictive capability of eacharticle characteristic.

FIG. 20 is a flow chart providing a method allowing for population of acorrelation coefficient table.

FIG. 21 is a flow chart illustrating a method associated with use of thepresent invention according to one embodiment.

FIG. 22 illustrates a Constraint Table according to an embodiment of thepresent invention.

FIG. 22A illustrates a Constraint Table according to another embodimentof the present invention.

FIG. 23 is a flow chart, according to one embodiment of the presentinvention, setting forth a method directed to the generation of aConstraint Table.

FIG. 24 is a chart illustrating the regression model for a predictorcharacteristic and a predicted characteristic including the boundaryvalues associated with the regression model.

FIGS. 25A thru 25G are charts illustrating the regression models betweena predictor characteristic and a predicted characteristic and illustratevarious circumstances where a defect condition can exist.

FIGS. 26A and 26B are charts illustrating the regression models betweena predictor characteristic and a predicted characteristic and illustratevarious circumstances where the predicted characteristic is robustwithin the upper and lower specification limits of the predictedcharacteristic.

FIGS. 27A thru 27F are charts illustrating the regression models forvarious circumstances where either the upper, the lower, or bothprediction boundaries constrain the minimum and/or maximum allowablepredictor characteristic value in order to produce parts that are withinspecification limits.

FIG. 28A is a flow chart setting forth a method directed to thegeneration of an Offset Table according to one embodiment of the presentinvention.

FIG. 28B is an Offset Table according to one embodiment of the presentinvention.

FIG. 29 is a flow chart illustrating the overall process associated withgeneration of a Relaxation Table according to one embodiment of thepresent invention.

FIG. 30 is a Relaxation Table generated by one embodiment of the presentinvention.

FIG. 30A is a second Relaxation Table generated by one embodiment of thepresent invention.

FIG. 30B is a Pmin Relaxation Table including a triangular array showingthe resulting tolerance limits for each predicted characteristic forsuccessive relaxations of Pmin.

FIG. 30C is a Pmax Relaxation Table including a triangular array showingthe resulting tolerance limits for each predicted characteristic forsuccessive relaxations of Pmax.

FIG. 30D is a flow chart illustrating a method, according to anembodiment of the present invention, directed to computation of the PmaxRelaxation Table of FIG. 30C.

FIG. 30E is a flow chart illustrating a method, according to anembodiment of the present invention, directed to computation of the PminRelaxation Table of FIG. 30B.

FIG. 31 is a flow chart illustrating a method directed to thedetermination of new specification limits resulting from the relaxationof one or both of the lower and upper specification limits of apredicted characteristic and, consequently, the associated minimumand/or maximum predictor characteristic values required to produce partswithin specification limits.

FIGS. 32A, 32B, 32C and 32D are charts that graphically illustrate theincrease in Pmax and the decrease in Pmin that result from relaxing theupper and lower specification limits for a predicted characteristic.

FIGS. 33A, 33B, 33C and 33D are charts that graphically illustrate thecompliance area, the regression area, the bounded regression area, and acomparison of the compliance area to the bounded regression area.

FIG. 34 is a Rankings Table providing the ranking of all articlecharacteristics from statistically most desirable predictor tostatistically least desirable predictor.

FIG. 35 shows a dialog box allowing the user to elect the option ofoverriding the selection of the statistically-best predictorcharacteristic.

FIG. 36 provides a dialog box allowing the user to input a predictorcharacteristic to override the statistically-best predictor.

FIG. 37 is a Rankings Table showing a list of unranked articlecharacteristics, a list of the ranked article characteristics, thestatistically-best predictor characteristic, the data column number ofthe statistically-best predictor characteristic, the user-selected,override predictor characteristic and the data column number of theuser-selected, override predictor characteristic.

FIG. 38 provides a simulated Offset Table according to an embodiment ofthe present invention.

FIG. 39 is a flow chart showing the overall process flow associated withthe process simulation functionality according to embodiments of thepresent invention.

FIG. 40 illustrates a scatter plot with the predictor characteristicalong the abscissa and the predicted article characteristic along theordinate.

FIG. 41 illustrates one embodiment of format for inputting engineeringdesign targets and tolerances into process analysis system 100.

FIG. 42 illustrates a prior art methodology, which can givecontradictory results when determining corrections for offsets.

FIG. 43 illustrates how the current invention explains how contradictoryresults can occur when determining offsets using prior artmethodologies.

FIG. 44 illustrates the determination of offsets according to anembodiment of the current invention.

FIG. 45 shows a regression line for two article characteristics andvarious offsets of the regression line from the target intersection.

FIG. 46A shows a regression model between a first article characteristic(P) and a second article characteristic (C), as well as the targetintersection and upper and lower specification limits for the articlecharacteristics.

FIG. 46B illustrates a simulation of the process modeled in FIG. 46Awith a modification to an upper specification limit for the secondarticle characteristic (C).

FIG. 46C illustrates a simulation of a process for which the designtarget for the second article characteristic (C) has been modified.

FIG. 46D illustrates a simulation of a process for which a pre-processparameter, such as the dimension of tooling used in the process,corresponding to the second article characteristic (C) has beenmodified.

FIG. 47 illustrates, for didactic purposes, the x- and y-intercepts, aswell as the slope, of a regression line.

DESCRIPTION OF PREFERRED EMBODIMENT(S) I. Background and OperatingPrinciples

A. Principles and Concepts

The present invention utilizes several graphical, statistical andmathematical techniques directed to analyzing the relationship betweenarticle characteristics to achieve a novel design and manufacturingprocess analysis system. Among these are scatter diagrams, correlationcoefficients, coefficients of determination, linear, non-linear andmulti-variate regression, prediction intervals, adjusted predictionintervals, prediction using regression, prediction using predictionintervals, DOE, averages and weighted averages. FIG. 3 is a processdiagram illustrating how an aspect of the present invention differs fromprior art techniques. In a wide variety of manufacturing processes, andinjection molding in particular, there is often a strong relationshipbetween article characteristics resulting from a given process. Thepresent invention assesses the statistical strength of theserelationships and, when they are sufficiently strong, capitalizes ontheir existence to facilitate a variety of design, production andmeasurement tasks associated with manufacturing processes.

In understanding the difference between the prior art exemplified inFIG. 2 and the present invention exemplified in FIG. 3, it should benoted that the primary focus of FIG. 3 is the relationship that existsbetween the part characteristics. In the field of injection molding, thedifferent outputs (#1, #2, etc.) in FIG. 3 would typically refer todifferent part dimensions. In the present invention, the different partcharacteristics are not limited to dimensions, but could be any partattribute. In addition, the different part characteristics may in factinclude the same dimension across different parts produced in a singlecycle of a multi-cavity mold.

FIG. 4 graphically illustrates the relationship between two articlecharacteristics as set forth on a scatter diagram. FIG. 4, in one form,illustrates the relationship between a predictor characteristic andpredicted characteristic. As discussed below, the data points used togenerate the regression model are typically generated using at least twodifferent possible methods. The first method consists of generatingparts with no changes made to the process settings. This generallycorresponds to a normal production run. All processes are subject tovariation in control variables, environmental conditions, wear and tear,and many other factors. These influences on the process cause naturalvariation in the process output. The process output from this method isthen measured. One problem with this method is that the process ofmeasurement is like any other process in that the measurement processhas its own source of variations which result in measurement error. Ifthe size of the natural variation in the part characteristics is smallrelative to measurement error, then the measurement error will overwhelmthe natural variation. In this instance, it is unlikely that anystatistically significant correlations could be established between thepart characteristics.

Injection molding processes typically have relatively small naturalvariations as compared to typical measurement error. Consequently, thefirst method of generating injection molded parts for evaluation ofrelationships can be unproductive. Consequently, the second method forgenerating parts is more applicable for injection molded parts. However,other processes may exhibit sufficient natural variation in order to usethe above-identified method. According to a second method, variation inpart characteristics is induced. In the case of injection molding,variation is induced by deliberately changing process control settings.In this manner, the variation in part characteristic values becomeslarge relative to measurement error. Correlations, to the extent thatthey exist, then become apparent.

As previously mentioned, DOE is a method that assists in reducing anunmanageably large number of experimental conditions down to amanageable few experimental conditions. Since variation must be inducedin the field of injection molding, there is utility in using DOEtechniques to design an efficient experiment. Use of this method hasfurther utility in that there are commercially available computerapplications that efficiently analyze the data and report the results ofthe analysis. Thus, one beneficial by product of using DOE, is thatuseful information may be extracted from an experimental run. Inparticular, it is usually possible to identify at least one processcontrol setting that can be used to significantly affect the partcharacteristics of resulting output. The information obtained from DOEhas utility as it can be used to adjust a process control setting toachieve a desired change in the joint operating position of the partcharacteristic values along the regression model, as explained below.

There is a second advantage to inducing variation in an experimental runthat is not connected with any efficiency measure associated with usingDOE. This second advantage lies in the fact that the present invention,in one embodiment, identifies the process control settings that have thegreatest impact or influence on the part characteristics. The presentinvention may also rely, in part, on the experience of the injectionmolding press operators and associated manufacturing and processpersonnel to select those “thigh impact” control settings. It should benoted that in injection molding, the usual paradigm is to minimizechanges to the press settings. In contrast, the present invention seeksto maximize their impact for purposes of inducing part variation forfurther analysis. In-other-words, for the purpose of inducing variation,the present invention seeks out the “worst” control settings. The“worst” control settings from a production perspective become the “best”control settings from the perspective of inducing variation.

As previously noted, there are a large number, typically 22 or more,process control settings in the field of injection molding. The presentinvention, in one embodiment, incorporates “scientific” or “decoupledmolding” principles to identify the high impact press controls. As withDOE, it is not necessary to use “scientific”/“decoupled molding”principles, but it potentially provides additional identificationadvantages. Thus, when several, typically 3-5, of the highest impactcontrol settings are changed in the experimental run, the greatestamount of variation will be introduced into the part characteristics.This variation will be of two types. The first will be a translation ofthe joint operating position along the regression line. The second mayinduce scatter of the data points about the regression line. It isimportant to create a robust data set to yield, in turn, a robustregression model for prediction.

Finally, the use of DOE techniques provides additional information.Specifically, the use of DOE techniques to induce part variation furtherallows for an understanding of how the process control variables thatwere changed affect part characteristics and potentially how thesecontrol variables interact with each other.

As previously discussed, it is difficult to establish the relationshipbetween injection molding control settings and part characteristics forseveral reasons including the large number of control variables, thepotentially large number of part characteristics, simple interactions,complex interactions, non-linearities and other effects. One of thegreat utilities of this invention is that even though there may be manyprocess control variables that influence a part characteristic, andthose changes may influence any one part characteristic in a verycomplex manner, changes in these variables have a predictable effect onthe relationship between the predictor characteristic and at least oneremaining article characteristic. Accordingly, as discussed in moredetail below, the systems and methods of the present invention allowdesign engineers and process operators to rely on the values of apredictor characteristic in order to determine whether one or morepredicted characteristics complies with design specifications. Inaddition, the systems and methods of the present invention allow designengineers and process operators to focus on the predicted characteristicin efforts to adjust process output to comply with designspecifications. These and other advantages will become apparent from thedescription provided below.

The regression model of FIG. 4 assumes a straight-line relationshipbetween the two variables with all data points being on the straightline; however, a perfectly linear model is seldom achieved becauseperfect correlation is rare in the real world. FIG. 10 illustrates thescatter of data points on a scatter diagram. Although the data pointsexhibit scatter, they also indicate a strong trend or relationship. Inother words, by knowing the value of one of the two variables, it ispossible to predict the other variable with a relatively high degree ofaccuracy. As applied to the present invention, knowledge of the value ofthe predictor characteristic can yield reasonably accurate knowledge ofthe value of the predicted article characteristic. In practice, scatteramong the data points is caused by a number of factors. These includevariations caused by common cause noise, common cause fluctuations incontrol variables, common cause variations in the process inputs andcommon cause variations in the measurement system used to measure thepart characteristics. FIG. 10 also illustrates two parameters that aretypically used to define the regression model. These are the slope ofthe regression line and the Y-intercept; however, other parameters canbe used. The embodiment shown in FIG. 10 also illustrates a linearregression model. The present invention, however, is not limited to theuse of a linear model. A non-linear regression model, such as amulti-variate model can also be used in connection with the presentinvention.

FIG. 8 illustrates the addition of upper and lower prediction intervalsto the regression model. The area bounded by the prediction intervalsrepresents the feasible area of output of the process, as to the x-axisand y-axis characteristic, when natural variation and measurement errorare included. In other words, all of the complexities of the process are“eliminated” since they show up as the bounded area of feasible output.The complexities that are “eliminated” include the aforementionedprocess control variable simple interactions, complex interactions,non-linearities, etc.

Analysis of process output in this manner provides a variety of usefulinformation facilitating design and manufacturing processes. Forexample, FIG. 4, as well as others, also contains a representation ofthe intersection between the design target for the predictorcharacteristic and the predicted characteristic. Location of the targetintersection provides a great amount of useful information to designengineers and process operators, as it illustrates that, for situationillustrated by FIG. 4, it is impossible to intersect the targetintersection no matter how the process control settings are changed.

For didactic purposes, the description of preferred embodimentsprimarily details application of an embodiment of the present inventionto injection molding processes. The present invention, however, hasapplication to a variety of manufacturing processes, such as plating,semiconductor manufacturing, machining, and any other process wherematerial is added, subtracted, or otherwise changed in form orstructure. In addition, the present invention can be applied to aid thedesign of a manufactured article, the development of a process tomanufacture the article, and/or the reduction of measurement costs.Moreover, the present application has application to a variety ofarticles, including stand-alone articles or items, as well as articlesintended as components, elements or parts of a combination. Accordingly,the description of the preferred embodiments set forth herein refers to“articles” and “parts” interchangeably.

The present invention also has application in assessing the impact ofsources of variation other than variation caused by changes in presscontrol settings. Virtually any source of variation, if it causessufficient variation in the part characteristic value, can be assessed.Selected examples could include determining the effect of setup-to-setupvariation, determining the effect of press-to-press variation forinjection molding, determining temporal effects such as the impact ofseasonal effects and assessing the impact of different types of rawmaterial or the impacting of purchasing either raw material orcomponents from different suppliers.

In addition, embodiments of the present invention can be performedwithout the aid of a computing device, such as a personal computer, toperform various mathematical and statistical computations set forthherein. For a small number of article characteristics, it is entirelyfeasible to do all of the analysis and/or graphing by hand and/or with aspreadsheet. In a preferred embodiment, however, given the large amountsof data and computational requirements, various operations associatedwith the present invention are performed with a computing deviceconfigured to execute the operations described herein and displayresulting data on a user interface display.

B. Exemplary System Architecture

FIG. 15 is a simplified block diagram illustrating a system architectureaccording to one embodiment of the present invention. As FIG. 2provides, a system architecture includes process analysis application100 and operating system 130. Process analysis system 100 includes datainput module 102, regression module 104, correlation module 106, displaymodule 108, and interface application module 110. Data input module 102is operative to receive article characteristic data, as well as formatand store the data in a suitable format for operation by other modulesassociated with process analysis system 100. Regression module 104 isoperative to compute a regression model given a set of inputs.Correlation module 106 is operative to perform operations relating tothe correlations among article characteristics as more fully describedbelow. Display module 108, in one embodiment, is operative to generategraphical displays of regression and/or correlation relationships for agiven set of data, as well as other data elements as more fullydescribed below. Interface application module 110 is operative tocoordinate operation of the other modules associated with processanalysis system 100 based on commands received from a user.

In one embodiment, the above-described system architecture operates inconnection with computer hardware system 800 of FIG. 11. Operatingsystem 130 manages and controls the operation of system 800, includingthe input and output of data to and from process analysis application100, as well as other software applications (not shown). Operatingsystem 130 provides an interface, such as a graphical user interface(GUI), between the user and the software applications being executed onthe system. According to one embodiment of the present invention,operating system 130 is the Windows® 95/98/NT/XP operating system,available from Microsoft Corporation of Redmond, Wash. However, thepresent invention may be used with other conventional operating systems,such as the Apple Macintosh Operating System, available from AppleComputer Inc. of Cupertino, Calif., UNIX operating systems, LINUXoperating systems, and the like.

FIG. 11 illustrates one embodiment of a computer hardware systemsuitable for use with the present invention. In the illustratedembodiment, hardware system 800 includes processor 802 and cache memory804 coupled to each other as shown. Additionally, hardware system 800includes high performance input/output (I/O) bus 806 and standard I/Obus 808. Host bridge 810 couples processor 802 to high performance I/Obus 806, whereas I/O bus bridge 812 couples the two buses 806 and 808 toeach other. Coupled to bus 806 are network/communication interface 824,system memory 814, and video memory 816. In turn, display device 818 iscoupled to video memory 816. Coupled to bus 808 are mass storage 820,keyboard and pointing device 822, and I/O ports 826. Collectively, theseelements are intended to represent a broad category of computer hardwaresystems, including but not limited to general purpose computer systemsbased on the Pentium® processor manufactured by Intel Corporation ofSanta Clara, Calif., as well as any other suitable processor.

The elements of computer hardware system 800 perform their conventionalfunctions known in the art. In particular, network/communicationinterface 824 is used to provide communication between system 800 andany of a wide range of conventional networks, such as an Ethernet, tokenring, the Internet, etc. Mass storage 820 is used to provide permanentstorage for the data and programming instructions to perform the abovedescribed functions implemented in the system controller, whereas systemmemory 814 is used to provide temporary storage for the data andprogramming instructions when executed by processor 802. I/O ports 826are one or more serial and/or parallel communication ports used toprovide communication between additional peripheral devices which may becoupled to hardware system 800.

Hardware system 800 may include a variety of system architectures andvarious components of hardware system 800 may be rearranged. Forexample, cache 804 may be on-chip with processor 802. Alternatively,cache 804 and processor 802 may be packed together as a “processormodule”, with processor 802 being referred to as the “processor core”.Furthermore, certain implementations of the present invention may notrequire nor include all of the above components. For example, theperipheral devices shown coupled to standard I/O bus 808 may be coupledto high performance I/O bus 806; in addition, in some implementationsonly a single bus may exist with the components of hardware system 800being coupled to the single bus. Furthermore, additional components maybe included in system 800, such as additional processors, storagedevices, or memories.

In one embodiment, the elements of the present invention are implementedas a series of software routines run by hardware system 800 of FIG. 11.These software routines comprise a plurality or series of instructionsto be executed by a processor in a hardware system, such as processor802. Initially, the series of instructions are stored on a storagedevice, such as mass storage 820. However, the series of instructionscan be stored on any conventional storage medium, such as a diskette,CD-ROM, ROM, etc. Furthermore, the series of instructions need not bestored locally, and could be received from a remote storage device, suchas a server on a network, via network/communication interface 824. Theinstructions are copied from the storage device, such as mass storage820, into memory 814 and then accessed and executed by processor 802. Inone implementation, these software routines are written in the C++programming language and stored in compiled form on mass storage device820. However, these routines may be implemented in any of a wide varietyof programming languages, including Visual Basic, Java, etc. Inalternate embodiments, the present invention is implemented in discretehardware or firmware. For example, an application specific integratedcircuit (ASIC) could be programmed with the above described functions ofthe present invention.

II. Operation of Exemplary Embodiments

A. Generating a Set of Articles Having a Range of Variation as to aPlurality of Article Characteristics

As described above, the present invention assesses the relationshipbetween article characteristics associated with a set of articles havinga range of variation as to the article characteristics. According to oneembodiment of the present invention, a user generates a set of partshaving a range of variation as to a plurality of article characteristicsaccording to a given process. For example, a user may install aninjection molding tool in an injection molding machine and produce a setof articles. The set of articles, or a sample thereof, are then measuredor otherwise inspected or assessed as to the article characteristics ofinterest. The resulting set of data is then recorded (e.g., such as inan Excel spread sheet table) and used for subsequent analysis.

A variety of article characteristics can be measured and analyzed. Forexample, measured or otherwise determined article characteristics caninclude the dimensions of the article (e.g., length, height, width,circumference, overall diameter, etc. of the article or a given featureof the article), hardness, porosity, bowing, smoothness, voidcharacteristics (whether voids exists and their number), color,strength, weight, and any other article characteristic, includingperformance characteristics such as the spray pattern of a nozzle orflow rate through a hydraulic restrictor.

As discussed above, the present invention can be applied to a set ofarticles where variation of the article characteristics occurs naturallyor is induced by varying process control variables associated with theprocess that creates the article. When articles are produced withunchanged process control settings, there is typically little naturalvariation in the resulting article characteristics. This is particularlytrue for injection molded plastic parts. Measurement error may obscureor otherwise render unreliable the natural variation observed from agiven set of articles. If it is not cost effective to use more precisemeasuring instruments, then variation should be induced in the parts byvarying process settings. Accordingly, in a preferred embodiment,article variation is induced when measurement error is large compared tonatural part variation.

A.1. Inducing Variation

Article variation can be induced by varying process settings. To inducevariation in a preferred form, the operator varies the process settingsduring the manufacturing process and allows the process to come toequilibrium between setting changes before manufacturing parts formeasurement. In addition, the operator in a preferred embodiment of themethod selects the set or subset of process settings that induces thegreatest variability in the article characteristics of interest. In apreferred embodiment, the upper and lower limits for the processsettings are chosen such that the process produces parts without harmingthe process equipment or tooling. Moreover, in a preferred form, themagnitude of the changes in process settings is chosen to inducevariation across the full range between the article characteristic upperand lower specification limits for each of the article characteristicsof interest.

Selecting which process settings to vary can be accomplished usingseveral alternative methods. In one embodiment, settings can be selectedrandomly. In a second embodiment, settings can be selected based on theexperience of the process engineer or process operator. In the thirdembodiment, settings can be selected by a process expert. For example,many experts in plastic injection molding will recommend that moldtemperature, hold time and hold pressure are three process settings thatinduce significant variation in part characteristics. In a fourthembodiment, a number of settings can be varied using a Design ofExperiments (DOE) screening design. The results from this experiment canthen be used to determine which process settings have a significantimpact on part characteristics. The smaller number of high impactprocess settings can then be varied to induce variation in the partcharacteristics.

As to injection molding processes, part variation, in one embodiment,may also be induced by selecting and varying process control settingsusing scientific/decoupled molding techniques. Scientific/decoupledmolding techniques provide a method of reducing the large number ofpress settings down to three or four key variables. Furthermore,scientific/decoupled molding techniques can be used in conjunction withthe experience of the mold press operator to determine which presssettings should be varied. In a preferred embodiment, the set ofarticles produced comprises articles from an adequate number ofrepetitions at each set of process control settings.

In a preferred embodiment, Design of Experiments (DOE) methodology isemployed to generate a set of articles having a range of variation. DOEcan be used irrespective of whether the determination of which processsettings to vary is made using operator experience, decoupled/scientificmolding principles, or some combination thereof. DOE defines efficientexperimental setups that allow the extraction of the maximum amount ofinformation for a relatively small experimental effort. Once it has beendetermined which press settings to change, DOE defines efficientexperimental setups that allow the extraction of the maximum amount ofinformation for a relatively small experimental effort. This applies toboth the design of the experiment (e.g., combinations of processsettings, and the number of replications at each combination, etc.) andto the analysis of the data. A wide variety of known DOE techniques andavailable software tools can be used to design the experimental run thatinduces part variation.

As discussed in more detail below, the use of DOE to produce a set ofarticles for analysis provides “bonus” information that can be used,after analysis according to the present invention, to move a givenarticle output closer to target and to reduce variation in the articles.For example, such information allows the operator to adjust presssettings to accomplish the following during production: 1) move productoutput to target, and/or 2) minimize product variation, and/or 3)minimize cost, and/or 4) minimize press cycle time.

A.2. Receiving Article Characteristic Values

In one embodiment, the present invention is implemented by a computingdevice (such as a special-purpose or general purpose computer)configured to execute the functionality described herein. After a givenset of articles is produced and article characteristics are measured, ina preferred form, a suitably configured computing device, executing datainput module 102, receives the article characteristic values associatedwith the set of articles and stores them in memory.

FIG. 9 sets forth a graphical user interface provided by an embodimentof the present invention that allows a user to input a set of articlecharacteristic values. As FIG. 9 illustrates, an embodiment of thepresent invention allows the user to open a data input database andmanually provide the set of article characteristics into a table. Oneembodiment, however, allows the user to import article characteristicvalue data stored in various file formats, such as in an Excel® spreadsheet table, or any other suitable file format. In one form, data inputmodule 102 is further operative to validate the data set, such aschecking for blank cells and other validation methods.

In addition, as discussed below, data input module 102 is operative toreceive other data associated with operation of embodiments of theinvention. For example, data input module 102 is operative to receivetarget values, as well as upper and lower specification limits, for allor a subset of article characteristics. In one embodiment, such data isused to provide users the ability to assess the relationship betweenprocess output and design specifications for a given set of processinputs.

B. Assessing Relationship between Article Characteristics

To allow for an assessment of the relationship between articlecharacteristics associated with a set of articles, process analysisapplication 100, in one implementation, generates a set of scatterdiagrams each based on a pair of article characteristics. See FIG. 10.The set of scatter diagrams can represent all possible combinations ofarticle characteristics, or it can consist of a subset of all possiblecombinations.

In one embodiment, display module 108 generates graphical displaysincluding scatter diagrams for presentation on display device 818 toallow the user to visually assess the degree of correlation betweenarticle characteristics. See FIG. 10. In one form, the graphical userinterface presented on display device 818 allows the user to select,using keyboard and pointing device 822, a first article characteristicfor the x-axis and successively view the scatter diagrams based on thefirst article characteristic and the remaining article characteristicson the y-axis. The user can use the information gleaned from this visualinspection to assess the capability of the first characteristic to be anadequate predictor of the remaining article characteristics (see below).

B.1. Determining Regression Models Between Article Characteristics

Process analysis system 100 also includes regression module 104operative to determine the regression model between selected articlecharacteristics. As discussed above, display module 108 is operative togenerate a graphical display of regression models and present them ondisplay device 818. See FIG. 10. As FIG. 10 shows, the regression modelmay be plotted and displayed with (or optionally without) the underlyingdata points. In a preferred embodiment, regression module 104 computesregression models using “least squares” curve fitting methods. However,other methods can also be used. Although the various figures show alinear regression model, the regression model can be a linear, anon-linear (higher order polynomial) model or multi-variate.

The display of the relationship between two article characteristics inthis manner provides useful information to process operators, designengineers and others associated with the design and manufacture of thearticle. The slope (steepness) of the regression line can be used todetermine the relative sensitivity of article characteristics to changesin process settings. In addition, the slope (steepness) of theregression line can be used to identify article characteristics thatwill be more restrictive on (more sensitive to) the allowable range ofprocess settings when specification limits are considered (see below).

B.1.a. Locating the Target Intersection

As discussed above, the design of an article generally results in atarget value, as well as upper and lower specification limits, for eacharticle characteristic (or at least the critical articlecharacteristics). In one form, process analysis system 100 is operativeto determine the intersection of the target values for a pair of articlecharacteristics relative to the corresponding regression model. FIG. 4illustrates an exemplary regression model display including the targetintersection located relative to the regression model associated with afirst (predictor, see below) characteristic and a second articlecharacteristic.

As FIG. 4 illustrates, location of a target intersection allows for avisual and/or analytical determination of the direction and magnitudethat the regression line is offset from the target intersection for eacharticle characteristic. Moreover, as the regression model essentiallyrepresents all possible combination of process settings (that is,without changing a process input, such as changing the dimensions of amold cavity), the resulting diagram allows one to determine whetherproducing a part having a given pair of article characteristics attarget value is achievable.

In addition, as FIGS. 5 and 6 illustrate, the information provided byFIG. 4 facilitates the process of changing an aspect of the process(e.g., process inputs or control settings) to shift output closer todesign target. For example, as FIG. 5 illustrates, the operator canchange the combination of process control settings to shift the jointoperating position closer to a desired point along the regression model.In one embodiment of the present invention, the process control settingscan be changed to optimize the joint operating positions of more thantwo part characteristics. In addition, by changing process inputs, theregression line can be shifted to a position closer to the targetintersection or shifted to a position such that the regression linepasses through the target intersection. See FIG. 6. In one embodiment ofthe present invention, the process inputs can be changed to optimize theposition of more than one regression line. Lastly, as FIG. 7 provides,changes in both process control settings and process inputs can be usedto shift the part characteristic values closer to the targetintersection. In one embodiment of the present invention, changes inboth process control settings and process inputs can be changed tooptimize more than two part characteristic values.

In one embodiment, an Offset Table is created based on how far theregression line is located from (offset from) the target intersectionfor each article characteristic. The offset is presented in threeformats: in the X-direction, the Y-direction and the directionperpendicular to the regression line.

B.1.b. Specification Limits

Process analysis system 100 is also configured to locate the upper andlower specification limits for the Y-axis article characteristicrelative to the regression model between the Y-axis articlecharacteristic and an X-axis article characteristic. See FIG. 12. Thisgraphical representation allows the ability to determine whether any ofthe Y-axis article characteristics are robust against changes to processvariables. In such cases, the regression line will generally have asmall slope and/or not intersect either the upper or lower Y-axisspecification limits.

In addition, process analysis system 100 is also operative to locate theupper and lower specification limits for the X-axis articlecharacteristic relative to the regression model. This representationallows one to determine whether the regression line passes through theacceptable region bounded by the four specification limits. In otherwords, this representation allows for a determination as to whether itis even possible, given the current process and process inputs, tomanufacture the parts within specification limits. In addition, locatingthe specification limits relative to the regression model allows for adetermination of the maximum and minimum values (and, therefore, range)for the X-axis characteristic that will yield articles where the Y-axischaracteristic is within specification limits. This range determinationallows a manufacturer, for example, to determine if the part is incompliance with the specification limits for both the X- and Y-axischaracteristics only by measuring the X-axis characteristic. To computethe minimum X-axis article characteristic, process analysis system 100computes the value of the X-axis article characteristic at which theregression model intersects the lower specification limit for the Y-axischaracteristic. Similarly, to compute the maximum X-axis articlecharacteristic, process analysis system 100 computes the value of theX-axis characteristic at which the regression model intersects the upperspecification limit for the Y-axis characteristic. In either case, theX-axis characteristic can be no larger than the upper specificationlimit for X and can also be no smaller than the lower specificationlimit for X.

B.1.c. Prediction Intervals

As FIG. 8 shows, process analysis system 100 may also add upper andlower prediction intervals to the regression model diagram to allow fora determination of the magnitude of the variability about the regressionmodel. In one embodiment, regression module 104 is further operative tocompute upper and lower prediction intervals based on a set of articlecharacteristic value pairs using known statistical methods. As FIG. 8illustrates, locating the prediction intervals also allows for anevaluation of the variability relative to the target intersection. Forexample, the target intersection may lie outside of the predictionintervals on either the high or low side. In this case, it is virtuallyimpossible to ever hit the target intersection given the same processinputs. For example, assuming that FIG. 8 models the relationshipbetween two article characteristics resulting from an injection moldingprocess, locating the target intersection reveals that use of the mold,in its current state, will not obtain a part on target as to the twoarticle characteristics. Further, when the target intersection lieswithin the prediction intervals, the percentage of parts where thearticle characteristic is greater than target and less than target canbe determined through the use of known statistical techniques.

In addition, prediction intervals may also be used in the determinationof minimum and maximum values for the X-axis characteristic (see SectionII.B.1.b., supra). As FIG. 13 illustrates, to compute the minimum X-axisarticle characteristic, process analysis system 100 computes the valueof the X-axis article characteristic at which the lower predictioninterval intersects the specification limit for the Y-axischaracteristic. Similarly, to compute the maximum X-axis articlecharacteristic, process analysis system 100 computes the value of theX-axis characteristic at which the upper prediction interval intersectsthe upper specification limit for the Y-axis characteristic. In eithercase, the X-axis characteristic can be no smaller than its lowerspecification limit and no larger than its upper specification limit. Anembodiment of the present invention allows the user to determine themagnitude of the prediction intervals by inputting the percentage ofarea in the distribution that the user wants to have included in betweenthe prediction intervals.

B.2. Predictor Characteristic

An embodiment of the present invention applies correlation andregression analysis to determine predictor characteristics inmanufacturing processes. In one embodiment, a predictor characteristicis selected from the plurality of article characteristics associatedwith a part and used as the single X-axis characteristic. As discussedin more detail below, the predictor characteristic is chosen based on anassessment of the capability of a given article characteristic to be apredictor of other article characteristics. The selection of a predictorcharacteristic, therefore, reduces the number of article characteristiccombinations that must be analyzed to a relatively small subset. Forexample, a part having 31 article characteristics would require analysisof over 900 relationships between article characteristics. The selectionof a predictor characteristic reduces this to 30 combinations. Inaddition, the selection of a predictor characteristic can be used in avariety of ways to facilitate design, production, and measurement tasksassociated with manufacturing. For example, a predictor characteristiccan be used to greatly reduce the time and expense associated withmeasuring parts, as only the predictor characteristic needs to bemeasured during production to determine if all other articlecharacteristics are within specification.

FIG. 16 illustrates a method involving selection of a predictorcharacteristic according to an embodiment of the present invention. Asdiscussed above, data input module 102 is operative to receive and storearticle characteristic data associated with a set of articles (e.g.,article characteristic values and design targets/specification limits)(step 202). In one embodiment, correlation module 106, as discussed inmore detail below, is operative to perform calculations (e.g., such asthe determination of correlation coefficients between all combinationsof article characteristics, computation of overall predictive capabilityof each article characteristic, etc.) to rank article characteristicsaccording to their relative predictive capabilities. In one embodiment,display module 108 displays the ranked list of article characteristicsand allows for selection of an article characteristic as the predictorcharacteristic (see step 204). As discussed below, a user may choose apredictor characteristic based on a number of considerations includingrelative predictive capability, feasibility/cost of measuring thearticle characteristic, etc. Still further, the selection of a predictorcharacteristic may be based on other methods (see below).

With a selected predictor characteristic, interface application module110 directs regression module 104 to determine the regression modelbetween the predictor characteristic (in one embodiment, as the x-axischaracteristic) and all or a subset of the remaining articlecharacteristics (see steps 206, 208, 210 and 211). When complete, theuser is prompted to select a predicted article characteristic (step212). Display module 108, in one embodiment, based on the equationdefining the regression model, generates a graphical display of theregression model between the predictor characteristic and the selectedpredicted characteristic (step 214).

In addition to the regression model, display module 108 is furtheroperative to add additional features to the graphical representationpresented to users. FIG. 17 illustrates a method for generating agraphical representation illustrating the relationship between apredictor characteristic and a predicted characteristic with additionalfeatures discussed above. Display module 108 retrieves the regressionmodel between the selected predicted characteristic and the predictorcharacteristic (step 302). As FIG. 17 provides, display module 108 mayalso locate the intersection of the target values associated with thepredictor and predicted characteristics relative to the regression model(step 304) (see Section II.B.1.a., supra). Display module 108 may alsolocate the predictions intervals associated with the regression model onthe display (step 306) (see Section II.B.1.c., supra). Still further,display module 108 may locate the upper and lower specification limitsassociated with the predicted characteristic (step 308), as well as theupper and lower specification limits associated with the predictorcharacteristic (step 310). See Section II.B.1.b., supra. Display module108 may also graphically illustrate the minimum and maximum values forthe predicted characteristic based on the specification limits and,optionally, prediction intervals (step 312). See Sections II.B.1.b. &II.B.1.c., supra.

A variety of interface displays are possible. For example, the equationdefining the regression model may be displayed to the user. Moreover,the maximum and minimum predictor characteristic values may be displayedto the user, as well as any other data associated with the articlecharacteristics and/or the relationship between them. In one embodiment,the graphical user interface presented on display device 818 allows theuser to select which of the above graphical elements to display.

B.2.a. Selecting Predictor Characteristic

The predictor characteristic can be selected using either a heuristic ora statistically-based approach. Moreover, the selection of a predictorcharacteristic may be based on a visual assessment of the correlationsbetween article characteristics or an analytically-based assessment.

B.2.a.1. Graphical Selection

In one embodiment, a user can use the scatter diagrams to visuallyassess the degree of correlation, amounting to a visual estimation ofthe correlation coefficient for each scatter diagram. The closer theboundary or perimeter around the data points approaches a straight line,the higher the correlation coefficient. The exception to this generalrule is for situations where the regression line is horizontal, ornearly so. See Section II.B., supra. The user can assess the scatterdiagrams of all possible combinations of article characteristics.However, in another embodiment, the number of scatter diagrams usedcould be greatly reduced by picking one article characteristic to act asthe foundational variable. Using the foundational variable as the X-axisvariable, a scatter diagram would be then created for each remainingarticle characteristic, which would be plotted on the Y-axis. Pickingthe “foundational” (equivalent to the predictor) article characteristiccan be based on looking at the “scatter” of the data, or can be randomlypicked. While a visual assessment may be practical if a small number ofarticle characteristics are involved, larger numbers of articlecharacteristics, resulting in combinations into the thousands, requires(at least for practical purposes) the use of a computing device toanalytically select the predictor characteristic.

B.2.a.2. Analytical Selection of Predictor Characteristic

To facilitate selection of a predictor characteristic, in oneembodiment, correlation module 108 calculates the correlationcoefficients between all or a subset of the article characteristics;determines, based on the calculated correlation coefficients, a valueindicating the predictive capability of a first article characteristicrelative to all other article characteristics; and repeats this processfor all or a subset of the article characteristics. FIG. 18 provides amethod illustrating a process flow associated with selection of apredictor characteristic. As FIG. 18 shows, correlation module 106, asmore fully described below, calculates the correlation coefficientsbetween all or a selected subset of the article characteristics (basedon a set of article characteristic values, see FIG. 19, Section A) andpopulates a correlation coefficient table (FIG. 19, Section B) (step402). Correlation module 106 then computes a value indicating therelative predictive capability of each article characteristic (step404). In one embodiment, this value is the average of the absolutevalues of the correlation coefficients for a given articlecharacteristic (see FIG. 19, Section C). Of course, other methods forcomputing this value can be used, such as computing the average withoutabsolute values, computing a weighted average, etc.

Correlation module 106 then ranks the article characteristics accordingto the values computed in step 404 (step 406). Display module 108 thendisplays the ranked list on display device 818 to allow the user toselect a predictor characteristic based at least in part on thepredictive capabilities of the article characteristics (step 408).According to one embodiment, the user makes his choice (step 410),causing interface application module 110 to direct regression module 104to compute the regression models between the selected predictorcharacteristic and the remaining article (predicted) characteristics(see above).

The correlation coefficient table may be populated using any suitableprocess or technique. However, in a preferred embodiment, correlationmodule 106 executes the methodology described below.

B.2.a.3. Population of Correlation Coefficient Table

According to standard industry practice, the data for a single articlecharacteristic (e.g., one dimension) is vertically arranged in a column.Thus, each column stores the data for one and only one articlecharacteristic. The resulting measurement data array will, therefore,have as many columns as there are article characteristics. More oftenthan not, the data is stored in an Excel spreadsheet, or other suitablefile format.

With this convention, then, each column represents a different articlecharacteristic. Each row represents the article characteristic data(multiple article characteristics) for a single part. In the case ofinjection molding, each row stores the data associated with a singlepress operating cycle. If the mold is a single-cavity mold, each rowwill contain measurement data for a single part. However, if the mold isa 4-cavity mold, each row stores the measurement data for all four partsproduced during one machine cycle. Typically, the same articlecharacteristics are measured for each part in a multi-cavity mold;however, there is no constraint that requires this.

In one embodiment, correlation module 106 includes functionality todetermine the correlation coefficients (according to standardstatistical methods) among all article characteristics, compute a valueindicative of the predictive capability of each article characteristic,and rank the article characteristics according to their relativepredictive capabilities.

FIG. 20 illustrates a method for populating the correlation coefficienttable discussed above. As FIG. 20 shows, in one embodiment, correlationmodule 106 initializes a correlation coefficient table (step 502) andvariables associated with the cell parameters of the table (A,B) (step504) and the article characteristics (see steps 506 and 508). Fordidactic purposes, assume that correlation module 106 operates on thearticle characteristic values of FIG. 19, Section A. In one embodiment,correlation module 106 computes the correlation coefficient between thefirst article characteristic (X=1) and the second article characteristic(Y=2) based on the article characteristic values in the correspondingcolumns (step 510). Correlation module 106 then stores the computedcorrelation coefficient (in the example, 0.999232) in the upper lefthand corner of the table (A=1, B=1) (step 512). Correlation module 106then calculates the correlation coefficients between the first articlecoefficient (X=1) and the remaining coefficients (Y), increasing the rowposition (B) with each successive computation and store (see steps 514,516 and 518).

After correlation module 106 reaches the last remaining articlecharacteristic (step 514), it fetches the computed correlationcoefficients in the first column (A=1), transposes the column into arow, shifts the row by one relative to the correlation coefficient tableand stores the data in the appropriate cells of the table (step 518).Correlation module 106 then increments the cell column position (A=2)and the article characteristic identifiers (X=2) (step 522), (Y=3) (step508) and sets the cell row position equal to the column position (B=2)(step 524). Correlation module 106 then computes the correlationcoefficient between the second article characteristic (X=2) and thethird article characteristic (Y=3; step 508) (step 510) and stores it inthe appropriate cell (A=2, B=2) (step 512). Correlation module 106repeats this process until the correlation coefficient between thesecond-to-last article characteristic value and the last articlecharacteristic value has been computed and stored (see step 520). AsFIG. 19 illustrates, the resulting columns of correlation coefficients,each column corresponding to an article characteristic, allows forrelative easy computation of a value (e.g., an average) indicating thepredictive capabilities of the article characteristics (see FIG. 19,section C).

As is apparent from the above-provided description, the process forpopulating the correlation coefficient table achieves a 50% reduction inthe number of correlation coefficients that must be computed, becausefor every XY correlation, there is a corresponding YX correlation. It isalso apparent that the compact notation of the correlation coefficienttable greatly facilitates programming the sub-routine that populates thetable. If the computations were to be done with only two correlations ineach row (XY and YX), there would be over 600 rows for 50 partcharacteristics. The preceding method for populating the correlationcoefficient table is one embodiment of the table population/compressionalgorithm. As previously mentioned, to maintain the usual and customaryconvention consistent with industry standards, the data for a singlearticle characteristic is vertically arranged in a column. The methodsdescribed herein would function equally well if the data for a singlearticle characteristic was horizontally arranged in a row and thealgorithm was adapted for that data structure. For that case, theaverage correlation coefficient would be computed by taking the averageof a row of correlation coefficients rather than a column.

B.2.a.4. Alternative Embodiment

In one embodiment, a user may use the functionality discussed above tocomplete the selection of a predictor characteristic and view thescatter diagrams with the predictor characteristic as the x-axisvariable and including specification limits, and optionally predictionintervals. Based on such scatter diagrams, the user may select thepredicted characteristics that are robust (insensitive to changes inprocess settings) and eliminate such article characteristics from thedata set to eliminate “noise.” This selection can also be accomplishedanalytically based on the slope and Y-intercept of the regression model,the location and slope of the prediction intervals and the value of theupper and lower specification limits for both the x-axis and y-axisvariables. In one form, such article characteristics have predictionintervals that do not intersect the predicted article characteristicspecification limits. In this context, they constitute “noise” inselection of a predictor characteristic. The user then re-runs theselection of the best predictor based on the revised (reduced) data set.

B.2.a.5. Overriding The Statistically Best Predictor

In one embodiment of the present invention, process analysis system 100allows the user to override the selection of the statistically bestpredictor characteristic. FIG. 34 illustrates the ranking of alldimensions from statistically most desirable to statistically leastdesirable in the Rankings Table. In one embodiment of the presentinvention, after process analysis system 100 creates and displays theunranked and ranked dimensions in the Rankings Table. As illustrated inFIG. 35, a dialog box asks the user if the user wants to override theuse of the statistically best predictor. If the user elects to overridethe statistically best predictor, a dialog box, in one embodiment of thepresent invention, accepts, as illustrated in FIG. 36, a data columnnumber designation that identifies the user-selected predictordimension. In one embodiment of the present invention, process analysissystem 100 displays the statistically best predictor, the data columnnumber of the statistically best predictor, the user-selected predictordimension, and the data column number of the user-selected predictor, ifany, in the Rankings Table as illustrated in FIG. 37.

B.2.b. Minimum and Maximum Values for Predictor Characteristic

To facilitate understanding of an embodiment of the present invention,it is useful to consider a simple situation where there are only twoarticle characteristics of interest. One of the article characteristicsis selected as the predictor characteristic using methods describedabove. The regression model establishes the relationship between thepredictor characteristic and the remaining article or predictedcharacteristic. This situation is illustrated in FIG. 12. Theintersection of the regression model with the upper and lowerspecification limits of the predicted characteristic (Y) determine thevalues of the predictor characteristic (X) above which the predictedcharacteristic does not meet specification. See Section II.B.1.b.,supra. The intersection between the regression model and the upperspecification limit for Y is defined as P-max. See FIG. 12. Theintersection between the regression model and the lower specificationlimit for Y is defined as P-min. It can readily be seen that as long asthe predictor characteristic (X) is between the values of P-min andP-max, then the predicted characteristic must be within specificationlimits. For this regression model, where there is perfect correlationbetween the predictor characteristic and the predicted characteristic,it can definitively be said that if the predictor characteristic isgreater than P-max or less than P-min, then the predicted characteristicwill be outside its specification limits. Another way of expressing thisis to define the distance between P-min and P-max as P-range. It canthen be said that as long as the predictor characteristic is withinP-range, then the predicted characteristic will be within specification.

Because the regression model seldom has a perfect degree of correlation,there is uncertainty in using one part characteristic to predict theother. The prediction intervals associated with the regression modelplace limits on the uncertainty associated with predicting one partcharacteristic given a value for the other part characteristic. FIG. 13illustrates use of the prediction intervals associated with theregression model to remove the effect of such uncertainties. As FIG. 13shows, two lines are shown located in the vicinity of and approximatelyparallel to the regression line. These lines are the upper and lowerprediction intervals that more or less bound the data points around theregression line. For didactic purposes, the upper and lower predictionintervals are shown as being straight lines; in practice, however, theyare generally curvilinear. As FIG. 13 provides, because of the scatterof the data points, the maximum allowable range for the predictorcharacteristic is more restricted. Specifically, the predictorcharacteristic can be no larger than the value associated with theintersection of the upper prediction interval and the upperspecification limit for the predicted characteristic, or the upperspecification limit for the predictor characteristic (whichever issmaller). In a similar fashion, the predictor characteristic can be nosmaller than the value associated with the intersection of the lowerprediction interval and the lower specification limit for the predictedcharacteristic, or the lower specification limit for the predictorcharacteristic (whichever is larger). In other words, P-max is the moreconstraining (smaller) of the upper specification limit for thepredictor characteristic and the intersection of the upper predictioninterval with the upper specification limit for the predictedcharacteristic. Similarly, P-min is the more constraining (larger) ofthe lower specification limit for the predictor characteristic or theintersection of the lower prediction interval with the lowerspecification limit for the predicted characteristic. As long as thepredictor characteristic is between P-min and P-max, the predictedcharacteristic will be within its specification limits. In oneembodiment, it can be a matter of judgment as to how “wide” theprediction intervals should be. In one embodiment, process analysisapplication 100 uses typical “width” parameters as a default setting.However, the user will have the option of overriding these defaultsettings.

B.2.c. Constraint Table for Predictor Characteristic

As discussed above, the foregoing discussion in section II.B.2.b.,supra, involved a simplified situation involving only two articlecharacteristics. In actual practice, a given part often has a largenumber of article characteristics of interest. In one embodiment,process analysis system 100 is further operative to create a ConstraintTable. The Constraint Table contains, for each predicted articlecharacteristic, the minimum (P-min) and maximum (P-max) values for thepredictor characteristic as determined above (see Sections II.B.1.b. &c., II.B.2.b., supra).

From the Constraint Table (see FIG. 22), the most constraining minimum(P-min*) and maximum (P-max*) values can be determined for the predictorcharacteristic. See FIG. 14; That is, the most constraining minimumvalue (P-min*) is the largest minimum value (P-min) in the ConstraintTable, while the most constraining maximum value (P-max*) is thesmallest maximum value (P-max) in the Constraint Table. FIG. 22illustrates a Constraint Table according to an embodiment of the presentinvention, where “none” means that the upper or lower specificationlimit for the predictor characteristic is the most constraining value asto the corresponding article characteristic. Accordingly, with anidentification of these most constraining minimum (P-min*) and maximumvalues (P-max*), a manufacturer can be confident that as long as thepredictor characteristic lies between them, the remaining predictedcharacteristics will be within specification limits.

As described more fully below, process analysis system 100, in oneembodiment, is operative, for each predicted article characteristic,to 1) determine whether the potential for defects exists; 2) determinewhether the predicted article characteristic is robust; and, 3) ifneither relationship 1) nor 2) is present, determine the maximum andminimum allowable values (Pmax and Pmin, respectively) for the predictedcharacteristic. In graphical and in various computational terms, processanalysis system 100 locates, relative to the regression model (and thearea it defines) between the predictor characteristic and the predictedarticle characteristic, a compliance area consisting of the area boundedby the upper and lower specification limits for both the predictedarticle characteristic and for the predictor characteristic (see FIG.33A). Process analysis system 100 then identifies a bounded regressionarea, which is the area bounded by the upper and lower predictionboundaries of the regression model and the upper and lower specificationlimits for the predictor characteristic. As FIG. 33B illustrates, theregression area is the region bounded by the regression model, includingthe prediction intervals 252 and 254, between the predictorcharacteristic and a predicted characteristic. FIG. 33C illustrates, abounded regression area, as defined above, bounded by the lower (LSL(X))and upper (USL(X)) specification limits of the predictor characteristic.

Process analysis system 100 then compares the bounded regression area tothe compliance area to assess the relationship between the two areas. Ifthe bounded regression area lies completely within the complianceregion, the predicted article characteristic is robust. As to thisrelationship, process analysis system 100 sets the minimum and maximumpredictor characteristic values associated with the first remainingarticle characteristic to the lower and upper specification limits,respectively, of the predictor characteristic. If any portion of thebounded regression area extends above and/or below the compliance areaover the specification limit range of the predictor characteristic, adefect potential exists, causing process analysis system 100, in oneembodiment, to report a defect condition. FIG. 33D illustrates acircumstance where a defect potential exists. Furthermore, if anyhorizontal segment of the bounded regression area extends completelywithin the compliance area and any second horizontal segment extendspartially or completely outside of the compliance area, the predictedand predictor characteristic have a constraining relationship. In theconstraining relationship, process analysis system 100 computes theminimum and maximum predictor characteristic values for the predictedcharacteristic.

Furthermore, one skilled in the art will recognize that 1) one of thethree possible relationships (robust, defect, and constraining)discussed above must exist, 2) the three possible relationshipsdiscussed above are mutually exclusive, and 3) if two conditions areknown not to exist, the third condition must exist. Accordingly, whenone of the possible relationships is identified as existing, processanalysis system 100 need not test for the existence of the otherrelationships. In addition, process analysis system 100 can test forthese relationships in any desired order.

As one skilled in the art will recognize, the are many ways to test forthe relationships discussed above. For example, process analysis system100 reports a defect potential as to the predicted articlecharacteristic, if all vertical cross-sections of the bounded regressionarea are fully or partially outside the compliance area. A predictedarticle characteristic can also be determined to be robust, if allvertical cross sections of the bounded regression area lie completelywithin the compliance area. Lastly, the predicted characteristicconstrains the predictor characteristic when at least one verticalcross-section of the bounded regression area is fully inside thecompliance area and at least one vertical cross-section of the boundedregression area is fully or partially outside the compliance area.

Process analysis system 100 can also determine whether the upper andlower prediction boundaries intersect the compliance area using knowncomputational methods. For example, if the upper and lower boundaries ofthe bounded regression area lie within the compliance area, thepredicted article characteristic is robust as to the predictorcharacteristic. If either or both of the upper and lower boundaries ofthe bounded regression area do not intersect the compliance area, then adefect potential exists. If the upper boundary of the bounded regressionarea intersects the upper boundary of the compliance area and the lowerboundary of the bounded regression area lies within the compliance area,a constraining relationship exists. Lastly, if the lower boundary of thebounded regression area intersects the lower boundary of the compliancearea and the upper boundary of the bounded regression area lies withinthe compliance area, a constraining relationship exists.

In another embodiment, process analysis system 100 can test to determinethe values of the upper and lower prediction boundary values at theupper and lower specification limits of the predictor characteristic anddetermine whether these values (coordinates) lie within the compliancearea. The following provides illustrative examples for didacticpurposes. FIG. 23 is a flow chart illustrating a method for generating aConstraint Table according to one embodiment of the present invention.In one embodiment, many variables and other inputs used in computing theconstraint table are taken from the output of other analyticalprocesses. For example, a correlation package and other procedurescompute the regression model and input the slope, intercept and boundaryinterval offsets of the regression model, in one embodiment, into anarray, such as a spread sheet file. The slope, intercept and boundaryinterval offsets as well as other previously computed values, are usedby process analysis system 100 to compute Pmin and Pmax and populate theConstraint Table. As FIG. 23 illustrates, process analysis system 100,starting with the first predicted characteristic (see step 702),computes the upper and lower prediction interval (boundary) values forthe predicted characteristic at the upper (USL) and lower (LSL)specification limits of the predictor characteristic (step 704). Fordidactic purposes, FIG. 24 illustrates the left upper boundary value241, the right upper boundary value 242, the left lower boundary value243 and the right lower boundary value 244. In one embodiment, a methodcalculates the boundary values at the upper and lower specificationlimits of the predictor characteristic based on the regression model,including the upper and lower prediction intervals, between thepredictor characteristic and the predicted characteristic. Processanalysis system 100 then determines whether these boundary values (seeFIG. 24) are within the four corners of the compliance area 250 definedby the upper and lower specification limits of the predictor andpredicted characteristics. In one embodiment, a method or function iscalled to determine whether the boundary values computed above exceedthe four corners of the compliance area 250. In one embodiment, thismethod returns four Boolean values corresponding to the respectivecorners of the compliance area 250 and indicate whether the boundaryvalues are within their respective corners. Process analysis system 100,in one embodiment, uses these Boolean values to determine whether thepotential for defects exist (step 706). FIGS. 25A-G graphicallyillustrate potential defect conditions between the predictorcharacteristic and a predicted characteristic (i.e., the potential thatthe predicted characteristic will exceed a specification limit withinthe specification limits of the predictor characteristic). FIGS. 25A-Gall include a compliance area 250 defined by the upper (USL) and lower(LSL) specification limits of the predictor and predictedcharacteristic. As FIGS. 25A-C illustrate, a defect may result from theupper prediction interval 252 failing to fall within the region boundedby the compliance area 250. FIGS. 25D-F provide examples of where thelower prediction interval 254 associated with the regression modelbetween the predictor and predicted characteristic does not fall withinthe compliance area 250. Lastly, FIG. 25G illustrates the circumstancewhere neither the upper prediction interval 252 nor the lower predictioninterval intersects the compliance area 250. In one embodiment, processanalysis system evaluates the Boolean values discussed above todetermine whether a defect condition exists. In one embodiment, if thetwo top corners, the two bottom corners, or all four corners areexceeded, a potential for defects exists. However, if only one corner ofeach pair is exceeded, there will be a constraining relation (seebelow).

If a defect potential exists, process analysis system 100 reports, inone embodiment, the defect potential by setting Pmin and Pmax to“DEFECT” as to the instant predicted characteristic (step 708). If nodefect condition is detected, process analysis system 100 thendetermines, in one embodiment, whether the predicted characteristic isrobust at least within the specification limit box 250 (step 710). Inone embodiment, a separate method or function tests for such robustnessby determining whether the boundary values discussed above lie withinthe specification limits of the predicted characteristic. In addition,FIGS. 26A and 26B graphically illustrate circumstances where thepredicted characteristic is robust relative to the predictorcharacteristic. If the predicted characteristic is robust, processanalysis system 100 sets Pmin to the lower specification limit of thepredictor characteristic and Pmax to the upper specification limit ofthe predictor characteristic (step 712).

Otherwise, if no defect condition is detected and the predictedcharacteristic is not robust, process analysis system 100, in oneembodiment, computes Pmin and Pmax (step 714). See Section II.B.2.b.,supra. As FIG. 23 illustrates, process analysis system 100 then repeatsthe process described above for all remaining predicted characteristics(see steps 716 and 718). FIGS. 27A thru 27F graphically illustratevarious circumstances where the upper and/or lower prediction intervalsconstrain Pmin and/or Pmax to values in between the lower and upperspecification limits of the predictor characteristic. As FIGS. 27A and27B illustrate, both the upper and lower prediction intervals associatedwith the regression model can constrain Pmin and Pmax. As FIGS. 27A and27B further illustrate, the slope of the regression model can influencewhether the upper or the lower prediction interval establishes ordetermines Pmin or Pmax. Furthermore, FIGS. 27C and 27D graphicallyillustrate circumstances where the upper predication interval constrainsPmin or Pmax. Similarly, FIGS. 27E and 27F graphically illustratecircumstances where the lower prediction interval constrains Pmin orPmax. In one embodiment, process analysis system 100 first determinesthe sign of the slope of the regression model prediction intervals.Process analysis system 100, in one embodiment, then determines thevalue of the predictor characteristic at the intersection of the upperprediction interval and the upper specification limit for the predictedcharacteristic. Similarly, process analysis system 100, in oneembodiment, also computes the value of the predictor characteristic atthe intersection of the lower prediction interval and the lowerspecification limit for the predicted characteristic. Process analysissystem 100 then determines whether the upper and/or lower predictioninterval constrains Pmin and/or Pmax and, if so, computes the value ofPmin and Pmax. In one embodiment, if the slope of the predictioninterval is positive, then process analysis system 100 sets the maximumpredictor characteristic value associated with the first remainingarticle characteristic to the lesser of the upper specification limit ofthe predictor characteristic or the value of the predictorcharacteristic at which the upper prediction interval corresponding tothe regression model intersects the upper specification limit for thefirst remaining article characteristic, and sets the minimum predictorcharacteristic value associated with the first remaining articlecharacteristic to the greater of the lower specification limit of thepredictor characteristic or the value of the predictor characteristic atwhich the lower prediction interval corresponding to the regressionmodel intersects the lower specification limit for the first remainingarticle characteristic. Otherwise, if the slope of the predictioninterval is negative, then process analysis system 100 sets the maximumpredictor characteristic value associated with the first remainingarticle characteristic to the lesser of the upper specification limit ofthe predictor characteristic or the value of the predictorcharacteristic at which the lower prediction interval corresponding tothe regression model intersects the lower specification limit for thefirst remaining article characteristic, and sets the minimum predictorcharacteristic value associated with the first remaining articlecharacteristic to the greater of the lower specification limit of thepredictor characteristic or the value of the predictor characteristic atwhich the upper prediction interval corresponding to the regressionmodel intersects the upper specification limit for the first remainingarticle characteristic.

B.2.c.1. Restricted or Constrained Article Characteristic Data

In one embodiment, the present invention changes it's computationalalgorithms when the user supplies constrained or restricted dimensionaldata. Data can be constrained when the data range over which variationwas induced is constrained by one or more factors. Examples of suchfactors might be personnel safety, harm to tooling, harm to processequipment, or manufacturing an incomplete part. For example, in aninjection molding process, pressures, temperatures or speeds could beconstrained to be below certain values. At pressures, temperatures orspeeds above these constrained values, in the didactic example, plasticcould be forced between mold halves (resulting in flash) and/or the moldcould be damaged. In-the-other direction, pressures, temperatures orspeeds could be constrained to be above certain values. At pressures,temperatures or speeds below these constrained values, insufficientplastic could be injected into the tooling resulting in a partiallyfilled or incomplete part. These constraining factors result indimensional data on the predictor dimension that have a maximum valuethat the user has decided will not be exceeded and/or a minimum valuethe user has decided the predictor dimension will not be less than.

In one embodiment of the present invention as illustrated in FIG. 9A,the user is given the option (see item #3) of designating whether or notthe data is constrained (at both the lower and upper values of thepredictor dimension in this didactic example). Other embodiments of thepresent invention give the user the option of designating whether or notthe data is constrained only at the lower value of the predictordimension or at only the upper value of the predictor dimension. If thisoption is not selected, the regression models between the predictorcharacteristic and the predicted characteristics are not constrained.

FIGS. 13A, 13B and 13C illustrate three situations where the predictordimension data can be constrained, as to a minimum value, a maximumvalue or with constraints on both the minimum and maximum values. FIG.13A illustrates one possible situation where the data is constrained ina manner that the largest data value for the predictor is less than thevalue that would have been computed for Pmax if the data were notconstrained. In this illustrative example, Pmax is set to the smaller ofthe maximum data value for the predictor or the calculated value of Pmaxif the data were not constrained. FIG. 13B illustrates one possiblesituation where the data is constrained in a manner that the smallestdata value for the predictor is greater than the value that would havebeen computed for Pmin if the data were not constrained. In thisillustrative example, Pmin is set to the larger of the maximum datavalue for the predictor or the calculated value of Pmin if the data werenot constrained. Lastly, FIG. 13C illustrates one possible situationwhere the data is constrained in a manner that (i.) the largest datavalue for the predictor is less than the value that would have beencomputed for Pmax if the data were not constrained, and (ii.) thesmallest data value of the predictor is greater than the value Pminwould have had if the data were not constrained. The user option in FIG.9A, item number 3 to select data that is constrained at both thesmallest and largest values of the predictor is consistent with FIG. 13Csince separate options are not illustrated in this embodiment for eithera maximum data value or a minimum data value.

FIG. 13D is a flowchart illustrating a method for adjusting the Pminand/or Pmax values associated with each predicted characteristic basedon a minimum and/or maximum constraint that the user has placed on thepredictor characteristic. As FIG. 13D illustrates, if the data isrestricted (880), process analysis system 100, in one embodiment,determines (882) whether the user has elected the option for a maximumdata value, MAX, for the predictor characteristic. If so, processanalysis system determines whether Pmax corresponding to a givenpredicted characteristic exceeds (888) the maximum data value or not. Ifso, process analysis system 100 sets Pmax to the maximum data value, MAX(890). As FIG. 13D illustrates, process analysis system 100, in oneembodiment, cycles through the Constraint Table computed above, startingwith the first predicted characteristic, PC (884), and continuingthrough for all predicted characteristics in the Constraint Table (886,892). If the user has elected the option for a minimum data value, MIN,for the predictor characteristic is specified (894), process analysissystem 100 performs a similar process setting Pmin, for all predictedcharacteristics where Pmin is less than the minimum data value, to MIN(896-904).

B.2.d. Determining Producibility Targets and Ranges

Further parameters can be derived from P-min* and P-max* that will beuseful for facilitating design of articles and process inputs and forsetting process control variables. The maximum allowable range(P-range*) can be computed by subtracting the most constraining minimumvalue (P-min*) from the most constraining maximum value (P-max*). FIG.14 graphically illustrates the determination of the most constrainingminimum and maximum values and range for the simplified case of twopredicted characteristics.

In addition, a predictor production target (P-target*) can bedetermined. P-target* is the point that one would pick as the target forthe average output of the process. It is, in one embodiment, the best“producibility” point that will maximize the chances of producing partsthat are in compliance with specification limits. When properlyselected, P-target* will minimize the percentage of data points that areoutside of P-range* during production.

To avoid confusion between design target and a target for the predictordimension (P-target*), a difference in terminology should be noted. Thepredictor characteristic (P) will almost always have an engineeringdesign target (P-target). The engineering design target (or nominalvalue) is a value called for by a design engineer (e.g., a number on adrawing or in a specification). In contrast, P-target* is a targetoperating point for the process that will optimize production output, asdiscussed above.

In one embodiment. the predictor production target (P-target*) isselected as the midpoint of P-range*. See FIG. 14. This would beappropriate for situations where the output of the production processwas symmetrical about its mean. Typical processes have a symmetricaldistribution that is approximately normal. If the distribution of thearticle characteristic is non-symmetrical, the target predictorcharacteristic value can be set to the average article characteristicvalue.

FIG. 22A illustrates a second embodiment of the Constraint Table andincludes a display of the computed values for (i.) the lower operatinglimit; (ii.) the upper operating limit; (iii.) the operating range;(iv.) the operating target; (v.) a printout indicating whether or notthe data is constrained; and (vi.) a designation of which dimension doesnot have a Pmin and Pmax because it is the predictor variable.

B.2.e. Offset Table

As discussed in more detail below, process analysis system 100, in oneembodiment, is further operative to generate an Offset Table indicatingthe amount by which a pre-process dimension would have to be adjusted toachieve a given design target. In other words, the Offset Table providesa value corresponding to the distance the regression line would have tobe moved to pass through the target intersection. For example, ininjection molding processes, an offset value is the amount by which agiven mold dimension would have to be changed (for example, by addingsteel through welding or removing steel through machining) so it wouldbe possible to achieve the target intersection between the predictordimension and a given predicted dimension). This is critical informationfor parts manufactured for the medical industry, for instance, wheremolds are sometimes modified to improve a part dimension by as little as0.001 inch. This information can be used by the mold designer and themold manufacturer. For other processes like plating, the Offset Tablecan tell engineers the required changes to the pre-plating dimensions ofthe part. For semi-conductor manufacturing, the Offset Table may informthe user the magnitude by which one or more mask dimensions must beadjusted. In addition, for processes involving CNC cutting or millingsystems, the pre-process settings may be the dimensions programmed intothe CNC application to create a desired article. The Offset Table caninform the engineers or CNC operators of the extent to which one or morecoordinates or other settings require adjustment.

In one embodiment of the present invention, the magnitude and directionof the offsets in the Offset Table are computed by determining thevertical distance between the target intersection and the regressionmodel. In a second embodiment of the present invention, the magnitudeand direction of the offsets in the Offset Table are computed bydetermining the horizontal distance between the target intersection andthe regression model. In a third embodiment of the present invention,the magnitude and direction of the offsets in the Offset Table arecomputed by determining the distance between the target intersection andthe regression model in a direction perpendicular to the regressionmodel.

The information contained in the Offset Table is also useful from yetanother perspective. Whether the predicted characteristic value isabove, at, or below the design target value for that predictedcharacteristic depends on the specific values selected for each of theprocess settings. In-other-words, the offset of the achieved predictedvalue from the design target value is dependent on the values selectedfor the process settings. For one set of process settings, the decisionmight be made to increase a mold dimension by removing steel. For adifferent set of process settings, the decision might be made todecrease that same mold dimension by adding steel. As can be imagined,this is an undesirable state of affairs. Using the new technologydescribed herein, offsets are computed by determining the distance anddirection of the regression model from the target intersection. Usingthe methodology described herein, offsets can be determined that areindependent from the values selected for the process settings.

FIG. 28A sets forth a method directed to the generation of an OffsetTable according to an embodiment of the present invention. FIG. 28Bprovides a resulting offset table according to an embodiment of thepresent invention. As FIG. 28A illustrates, process analysis system 100,in one embodiment, first initializes an offset table array (step 802).In one embodiment, the offset table is a two-column array includingarticle characteristic identifiers in one column and correspondingoffset values in a second column. The inputs to the offset table methodor function include the target values of the article characteristics andthe respective regression models between the predictor characteristicand the remaining predicted characteristics. As FIG. 28A provides,process analysis system 100, for all predicted characteristics (see step804), computes the value of the predicted characteristic from theregression model at the target value of the predictor characteristic(step 806). Process analysis system 100 then determines the offset valuefor the predicted characteristic by computing the difference between thecomputed value of the predicted characteristic and the target (designspecification) value of the predicted characteristic (step 808). Processanalysis system 100 then stores the resulting offset value in an arrayor other suitable data structure (step 810) and repeats the process forthe next predicted characteristic.

The Offset Table, in one embodiment, is particularly useful because itenables the tooling engineer to determine offsets independent of theparticular process settings chosen by the operator or process engineerto generate parts. This is illustrated in FIGS. 42-44. The Offset Tablehas great value when the tooling engineer is modifying pre-productiontooling into production tooling.

FIG. 42 represents the prior art situation. There is a criticaldimension (C) with an engineering design target value (C-TARGET).Operator #1 may choose process settings that result in the value for Cbeing less than C-TARGET. The tooling engineer will conclude that thetooling dimension for C is too small and that the tooling dimension mustbe increased. On-the-other-hand, Operator #2 may choose process settingsthat result in the value for C being greater than C-TARGET. The toolingengineer will conclude that the tooling dimension for C is too large andthat the tooling dimension must be decreased. Thus, the actions that thetooling engineer takes to modify the tooling is dependent on the processsettings chosen by the operator or process engineer. Further, theprocess engineer or operator (#3) may choose yet another set of processsettings in production. This would invalidate the tooling modificationdone on the basis of both Operator #1 and Operator #2.

This contradictory situation in the prior art is explained in FIG. 43.As illustrated in FIG. 43, the process settings chosen by both Operator#1 and Operator #2 fall on the regression line. FIG. 44 illustrates oneembodiment for calculating the offset using the vertical distancebetween the regression line and the target intersection.

B.2.f. Design Tolerance Relaxation Table

In many cases, it is faster and cheaper to relax the design toleranceson a given part instead of changing pre-process elements, such as thedimensions of a mold. The design engineer must, of course, make adecision as to whether or not this is feasible in light of the potentialimpact of tolerance relaxation for any given article characteristic onform, fit or function. Further, in some instances, the operating range(Prange*, supra) may be of such small size as to make it difficult,unlikely, or even impossible to produce parts or assemblies that haveall article characteristics within specification limits. If the decisionis made to increase producibility by increasing the operating range byrelaxing tolerances, then one embodiment of the Design ToleranceRelaxation Table facilitates an assessment of which design tolerances torelax by providing a prioritized list of the optimal order in whichtolerances should be relaxed, as well as an analysis of the increase inoperating range achieved for each incremental tolerance relaxation.

In one embodiment, process analysis system 100 is operative to generatea Design Tolerance Relaxation Table which facilitates analysis of theachievable gains in operating range associated with relaxing the designtolerances of each predicted article characteristic. The mostconstraining predicted characteristic (i.e., having the highest Pmin orlowest Pmax) should be relaxed first. Said differently, the firsttolerance to be relaxed is the tolerance on the article characteristicthat most constrains the predictor characteristic. The Design ToleranceRelaxation Table then tells the engineer how much increase there wouldbe in the operating range (the difference between P-min* and P-max*) asa result of relaxing that tolerance. The Design Tolerance RelaxationTable also tells the engineer the cumulative gain achieved by relaxingeach variable in turn.

FIG. 30 provides a Design Tolerance Relaxation Table according to oneembodiment of the present invention. As FIG. 30 illustrates, theRelaxation Table, in one embodiment, is divided into two main parts,namely, the ranked list of the article characteristics by mostconstraining Pmin value, and a ranked list of the same articlecharacteristics by most constraining Pmax value. In this example, Var12is the predictor characteristic and is placed in the last position inboth lists. For each article characteristic in either the Pmin or Pmaxcolumn, the Relaxation table includes the following fields: 1) anarticle characteristic identifier; 2) the calculated Pmin/Pmax value, 3)the individual gain in operating range achieved by relaxing thetolerance of the corresponding article characteristic, and 4) thecumulative gain associated with relaxing the tolerance of thecorresponding article characteristic. As one skilled in the art willrecognize from the description provided herein, the individual gainassociated with each article characteristic assumes that Pmin/Pmax hasbeen relaxed to the Pmin/Pmax values associated with the followingarticle characteristic. The cumulative gain corresponding to a givenarticle characteristic indicates the aggregate gain in operating rangeachieved by relaxing Pmin/Pmax to the value associated with thefollowing article characteristic. For example, to achieve a cumulativegain of 0.0030 inches in operating range, the specification limitscorresponding to article characteristics Var16, Var13, Var11, Var9 andVar10 should be relaxed to the point where Pmin equals 6.3741 inches.Alternatively, to achieve a 0.0043 inch cumulative gain in operatingrange, the specification limits corresponding to article characteristicsVar2 and Var4 should be relaxed to the point where Pmax equals 6.3819inches. Alternatively, a 0.0053 inch gain in operating range could beachieved by relaxing the appropriate tolerances on Var16, Var2 and Var4.A 0.005 inch gain in operating range can be very significant and helpfulin certain circumstances. For example, if the operating range was 0.005inches prior to tolerance relaxation, then a 0.005 inch increase equatesto doubling the operating range or, effectively, making it twice as easyto achieve process settings that produce article characteristics thatare within specification limits.

FIG. 29 illustrates an exemplary method directed to generating a DesignTolerance Relaxation Table according to an embodiment of the presentinvention. As FIG. 29 illustrates, process analysis system 100, in oneembodiment, formats or initializes a Design Tolerance Relaxation Table(step 830) and transfers the Pmin and Pmax values calculated above (seeSections II.B.2.b., c., above) to the relaxation table (step 832).Process analysis system 100 then calculates the individual gainsachieved by relaxing Pmin for the article characteristics (step 834). Inone embodiment, process analysis system 100 sorts the articlecharacteristics from most to least constraining (highest) Pmin value andthen adds the predictor characteristic identifier and associatedspecification limit to the end of the sorted list. Process analysissystem 100 then computes the individual gain achieved by relaxing thespecification limit for each article characteristic to the valuecorresponding to the Pmin of the next article characteristic in thesorted list. For example, the individual gain achieved by relaxing thespecification limit of Var16 to the level of Pmin corresponding to Var13is the difference between Pmin of Var16 and Pmin of Var13. In thisinstance, the difference is 0.0005 inches; the table of FIG. 30 shows0.0004 inches due to computer spreadsheet round-off of the values forP-min. The succeeding individual gains for the remaining articlecharacteristics are computed in a similar manner until the last(predictor) characteristic is reached.

Process analysis system 100 then calculates the individual gainsachieved by relaxing Pmax and, hence, the specification limits for thepredicted article characteristics (step 836). In one embodiment, processanalysis system 100 sorts the article characteristics by mostconstraining (lowest) Pmax value and then adds the predictorcharacteristic identifier and associated upper specification limit tothe end of the sorted list. Process analysis system 100 then computesthe individual gain achieved by relaxing the specification limit of agiven article characteristic to the value corresponding to the Pmax ofthe next article characteristic in the sorted list. For example, theindividual gain achieved by relaxing the specification limit of Var2 tothe level of Pmax corresponding to Var4 is the difference between Pmaxof Var2 and Pmax of Var4. This difference is 0.0006 inches; note thatthere is no computer round-off in this instance for the values of Pmax.The succeeding individual gains for the remaining articlecharacteristics are computed in a similar manner until the last(predictor) characteristic is reached. Process analysis system 100 thencomputes the cumulative gains associated with each successive tolerancerelaxation for both the Pmin and Pmax relaxations (step 838).

As one skilled in the art will recognize, the Design ToleranceRelaxation Table does not display the lower/upper specification limitsfor the predicted characteristics resulting from a relaxation of theselimits to a given Pmin or Pmax; rather, the Design Tolerance RelaxationTable set forth in FIG. 30 facilitates a determination of which and howmany article characteristic design tolerances to relax to achieve adesired gain in operating range.

In one embodiment, process analysis system 100 is operative to calculatethe resulting lower and/or upper specification limits after the user hasselected which article characteristic specification limits to relax. Forexample, the user may elect to relax only lower specification limits,only upper specification limits, or a combination of upper and lowerspecification limits to increase the allowable operating range(P-range*). In one embodiment, process analysis system 100 prompts theuser for a selection of an article characteristic from the Pmin columnand/or an article characteristic from the Pmax column of the DesignTolerance Relaxation Table and then calculates the new lower or upperspecification limits for each article characteristic in the sortedlist(s) up to the selected article characteristic(s). Specifically, foreach article characteristic in the sorted list up to the selectedarticle characteristic, process analysis system 100, using theregression model, calculates the article characteristic values where theboundaries (upper and lower prediction intervals) of the regressionmodel intersect Pmin or Pmax as appropriate. For didactic purposes,FIGS. 32A, 32B, 32C and 32D graphically illustrate the computation ofnew upper and lower specification limits from the relaxation of Pmin andPmax. As these Figures illustrate, the slope of the regression modeldetermines whether the relaxation of Pmin/Pmax results in a relaxationof the upper or the lower specification limit associated with a givenarticle characteristic. As FIG. 32A illustrates, for a regression modelwith a positive slope, the relaxation of USL(Y) to USL′(Y) results inthe relaxation of Pmax to Pmax′. The operating range will increase inmagnitude by an amount equal to Pmax′-Pmax. Similarly, FIG. 32Billustrates, for a regression model with a positive slope, that therelaxation of LSL(Y) to LSL′(Y) results in the relaxation of Pmin toPmin′. The operating range will increase in magnitude by an amount equalto Pmin-Pmin′. For regression models with a negative slope, therelaxation of USL(Y) to USL′(Y) results in the relaxation of Pmin toPmin′ (see FIG. 32C) and the relaxation of LSL(Y) to LSL′(Y) results inthe relaxation of Pmax to Pmax′ (see FIG. 32D).

As FIG. 31 illustrates, process analysis system 100, in one embodiment,receives a selected predicted characteristic from the Pmin and/or PmaxRelaxation column (step 850). If the user selects a predicted articlecharacteristic to relax Pmin (step 852), process analysis system 100sets Pmin to the Pmin value corresponding to the next articlecharacteristic in the sorted list (step 854). For example and withreference to FIG. 30, Pmin would be set to 6.3761 inches (correspondingto Var11), if Var13 was the selected predictor characteristic. Processanalysis system 100, for each article characteristic up to and includingthe selected article characteristic (see step 856), then computes theresulting new specification limits. Specifically, process analysissystem 100 determines the slope of the regression model to select thelower or upper prediction interval, as appropriate (see above) (step857) and computes the value at Pmin of the appropriate (e.g., lower orupper) prediction interval (boundary) of the regression model betweenthe predictor characteristic and the predicted characteristic (step 858)and stores the new specification limit in a table in association withthe corresponding predicted article characteristic (step 860) forultimate display to the user.

If the user selects a predicted article characteristic to relax Pmax(step 862), process analysis system 100 sets Pmax to the Pmax valuecorresponding to the next article characteristic in the sorted list(step 864). Process analysis system 100, for each article characteristicup to and including the selected article characteristic (see step 866),then computes the resulting new specification limits. Specifically,process analysis system 100 determines the slope of the regression modelto select the lower or upper prediction interval, as appropriate (seeabove) (step 867) and computes the value at Pmax of the appropriateprediction interval (boundary) of the regression model (step 868) andstores the new specification limit in a table or other data structure(step 870) for ultimate display to the user. As one skilled in the artwill recognize, use of the Design Tolerance Relaxation Table in thismanner will result in new Pmin* and/or Pmax* values.

In another embodiment, process analysis system 100 displays thetolerance relaxations resulting for all predicted characteristics up tothe predicted characteristic in a triangular array. The Constraint Tablein FIG. 30A is different from the Constraint Table in FIG. 30 in thatFIG. 30A displays analysis results from a different set of data thanthat used to generate FIG. 30. FIGS. 22A (Constraint Table), 30A(Relaxation Table), 30B (Pmin Tolerance Relaxation Table) and 30C (PmaxTolerance Relaxation Table) comprise a set of process analysis system100 output tables from a single data input table.

FIGS. 30 and 30A, in one embodiment of the present invention, illustratethe individual and cumulative increases in operating range that can beachieved by sequentially relaxing tolerances. The first five columnsdisplay improvements in operating range achieved by decreasing Pmin*.The last five columns display improvements in operating range achievedby increasing Pmax*. Tolerances are relaxed in the order (top to bottom)given in the Relaxation Table. One or more Pmin* tolerances can berelaxed; one or more Pmax* tolerances can be relaxed; or a combinationof Pmin* and Pmax* tolerances can be relaxed. In FIG. 30A (or 30B), forexample, one could relax the minimum tolerances on Variables 9, 10 and8. This would result in a 0.0079″ increase in operating range bydecreasing Pmin* by that amount. One could also (or alternatively) relaxthe maximum tolerance on Variables 2 and 4. As shown in FIGS. 30A (or30C), this would result in a 0.0011″ increase in operating range byincreasing Pmax* by that amount.

In one embodiment of the present invention, FIGS. 30 and 30A display theimprovements achievable in operating range. They do not display thetolerance relaxations required for each dimension. In one embodiment ofthe present invention, FIGS. 30B and 30C display the resultingtolerances required for each predicted characteristic. FIG. 30B displaysthe new tolerances required to reach each successive increase inoperating range based on relaxations of Pmin. FIG. 30C displays the newtolerances required to reach each successive increase in operating rangebased on relaxations of Pmax. As the tolerances are relaxed on eachsucceeding dimension, the tolerances on all prior dimensions must alsobe relaxed as specified by FIGS. 30B and 30C.

As discussed above, FIG. 30B is illustrative of gains in operating rangeachieved by decreasing Pmin*. Var9 is the most constraining dimension onP-min* (FIG. 30B). If the lower tolerance on Var9 is relaxed to−0.0155″, then the operating range will be increased by 0.0033″. Thesecond most constraining dimension on Pmin* is Var10. If the lowertolerance on Var10 is relaxed to −0.0142″, then the cumulative gain inoperating range will be 0.0052″. Since Var9 is more constraining onP-min* than Var10, if the tolerance on Var10 is relaxed then thetolerance on Var9 must be relaxed further than if only the tolerance onVar9 was relaxed. In this example, if the lower tolerance on Var 10 isrelaxed to −0.0142″, then the lower tolerance on Var9 must be relaxed to−0.0175″. Similarly, if the tolerance on Var8 is relaxed, then thetolerances on Var9, Var10 and Var8 must be relaxed to the values in therow starting with Var8. In addition, as discussed more fully below, ifthe slope of the regression model between the predicted characteristicand a given predicted characteristic is negative, then relaxations ofPmin result in relaxations of the upper tolerance for a given predictedcharacteristic. In the embodiment illustrated, negative tolerance valuesindicate lower tolerances, while positive values indicate uppertolerances for a give predicted characteristic. Similar to FIG. 30B,FIG. 30C is illustrative of gains in operating range achieved byincreasing Pmax*. In FIG. 30C for example, one must relax tolerances onVar2, Var4, and Var8 to achieve a 0.0013″ increase in operating range.The new values for the tolerances for each of these predictedcharacteristics is specified in the row starting with Var8 (in thisexample).

FIGS. 30A, 30B and 30C facilitate a determination of whether to decreasePmin* or increase Pmax* or to do both. For example, only one tolerance(Var9) needs to be relaxed to increase the operating range 0.0033″ bydecreasing Pmin*, while three tolerances need to be relaxed (Var2, Var4and Var8) to increase the operating range 0.0013″ by increasing Pmax*.FIGS. 22A (Constraint Table), 30A (Relaxation Table), 30B (PminRelaxation Table) and 30C (Pmax Relaxation Table) also facilitate adetermination of the relative increase in operating range achievable byrelaxing tolerances.

FIGS. 30D (Pmax) and 30E (Pmin) illustrate methods, according to anembodiment of the present invention, directed to creating the designtolerance relaxation tables of FIGS. 30C (Pmax) and 30B (Pmin),respectively. As the figures provide (and as more fully discussedherein), the methods, in one embodiment, adapt to the circumstance wherethe data values for the article characteristics are constrained.

FIG. 30D provides a method for computing the Pmax Relaxation Tableillustrated in FIG. 30C. As the following illustrates, process analysissystem 100 operates to create the triangular array of design tolerancesresulting from relaxations of Pmax. As FIG. 30D illustrates, processanalysis system 100 initializes a counter and determines the number ofarticle characteristics (930). Starting with the first articlecharacteristic (see 932), NewPmax, the value to which Pmax is relaxed,is set to the Pmax value, OldPmax, associated with the subsequentarticle characteristic (936). If the last row has been reached [I=N](934), NewPmax is set to OldPmax (938), since the last articlecharacteristic is the predictor characteristic. In the didactic exampleof FIG. 30C, NewPmax for Var2 (5.4650″) is the OldPmax value for Var4(5.4650″), while the NewPmax for Var5, the predictor characteristicremains at its OldPmax (5.4660″).

As FIG. 30D illustrates, process analysis system then calculates the newtolerances based on NewPmax. Process analysis system 100, in oneembodiment, determines the slope of the regression model between thecurrent article characteristic (Var2 in the first row of the PmaxTolerance Relaxation Table) and the predictor characteristic (944). Ifthe slope of the regression model is positive (948), process analysissystem 100 calculates the new Upper Specification Limit for the currentpredicted characteristic, J, by computing the value of the Y-interceptof the upper prediction interval or boundary plus the slope of theregression model times NewPmax(I) (950). FIG. 32A graphicallyillustrates this computation. The upper tolerance, UT, is the UpperSpecification Limit (USL) minus the target design value for thepredicted characteristic (951).

Process analysis system 100, in one embodiment, further accounts for thesituation where the data is constrained (see Section B.2.C.1, above) andthe computed tolerances may be less than the design tolerances. That is,if the data is constrained and the new upper tolerance is less than theoriginal upper design tolerance (952), process analysis system sets theUpper Specification Limit, USL(I,J), to the original design upperspecification limit, DesignUSL(I,J) (954) and the upper tolerance,UT(J), to the original design upper tolerance, DesignUT(J) (956).

As FIG. 30D further illustrates, if the slope of the regression model isnegative (948), relaxations of Pmax implicate the lower predictioninterval of the regression model, as well as the lower specificationlimits (LSLs) and lower tolerances of the predicted articlecharacteristics (See FIG. 32D). Accordingly, process analysis system 100calculates the Lower Specification Limit, LSL(I,J), by computing thevalue of the Y-intercept of the lower prediction interval or boundaryplus the slope of the regression model times NewPmax(I) (958). FIG. 32Dgraphically illustrates this computation. The lower tolerance, LT(I,J),is the new Lower Specification Limit, LSL(I,J), minus the target value,Target(J), for the current predicted characteristic (960). As above, ifthe data is constrained and the new lower tolerance limit, LT(I,J) isgreater than the original lower design tolerance, DesignLT(J), of thepredicted characteristic (or the absolute value of LT(I,J) is less thanthe absolute value of DesignLT(J)) (962), process analysis system 100sets the Lower Specification Limit, LSL(I,J), to the original designLower Specification Limit, DesignLSL(J) (964) and the Lower Tolerance tothe original design lower tolerance, DesignLT(J), for the predictedcharacteristic.

As FIG. 30D illustrates, the for-do loops (932, 940) and incrementingthe counter (968) create the triangular array (see FIG. 30C) oftolerance values for successive relaxations of Pmax. Furthermore, if thelast row has been reached (942), the new tolerance value is set to “N/A”or other suitable identifier.

FIG. 30E provides a method for computing the Pmin Relaxation Tableillustrated in FIG. 30B. As one skilled in the art will recognize,creation of the Pmin Relaxation Table proceeds in a similar manner tocreation of the Pmax Relaxation Table, above. As FIG. 30E illustrates,the slope of the regression model (948) determines whether the lowertolerances and lower specification limits (972, 974), or the uppertolerances and upper specification limits (976, 978), are implicated forrelaxations of Pmin. FIG. 32B graphically illustrates the resultinglower specification limit, LSL(I,J), for relaxations of Pmin where theslope of the regression model is positive. FIG. 32C graphicallyillustrates the resulting upper specification limit, USL(I,J), forrelaxations of Pmin where the slope of the regression model is positive.

C. Using the Maximum Allowable Range (P-range*) and the PredictorProduction Target (P-target*)

For didactic purposes, it will be useful to clarify and define certainparameters as follows:

-   -   1. P-range* is the maximum allowable range for the predictor        characteristic. It is the range within which the predictor        characteristic must be to ensure that the remaining part        characteristics will remain in compliance with specification        limits;    -   2. P-target* is the value of the predictor characteristic        production target value. P-target* can be set at several values        within P-range*. P-target* will usually be set at the midpoint        of P-range*;    -   3. VAR is the range of variability of the predictor        characteristic associated with actual process output under        production conditions. It is determined by assessing production        output;    -   4. X-BAR is the average value of the predictor characteristic        under production conditions. It is determined by assessing        production output;    -   5. P-target is the engineering design target value for the        predictor characteristic. It is determined by the design        engineer to optimize form, fit and function; and,    -   6. USL and LSL are the upper and lower specification limits for        the predictor characteristic based on the engineering design        tolerances. They are determined by the design engineer and        typically consider a number of factors including history        tolerances used by the organization, criticality of the part,        capability of the manufacturing organization, and other factors.

Knowing the maximum allowable range (P-range*) for the predictorcharacteristic is extremely useful. The actual process output willexhibit a certain amount of variability (VAR) and there will be a valuethat represents the predictor characteristic average process output(X-BAR). Having this information facilitates changing at least oneprocess control setting in a manner that maximizes the likelihood thatthe process generates parts that are in compliance with thespecification.

If it has been decided that P-range* is too “constrained”, then a secondpart characteristic can be measured to “open up” the “constraints” onP-range*.

There is also great utility in comparing the size of the actual processvariability (VAR) for the predictor characteristic to the maximumallowable range (P-range*). The following situations are possible:

-   -   1. If the actual process variability (VAR) is greater the        maximum allowable range (P-range*), then a portion of the parts        produced by the process will always be out of compliance.    -   2. If the actual process variability (VAR) is equal to the        maximum allowable range (P-range*) and the average process        output (X-BAR) is centered within the maximum allowable range,        then nearly all parts produced by the process will be in        compliance, but there will be no room for error or for shifts in        the process output.

If the actual process variability (VAR) is smaller than and lies withinthe maximum allowable range (P-range*), then nearly all parts will be incompliance and there will be a greater margin of safety against errorsor shifts in process output.

The present invention creates, for situation number 3. an excellentopportunity for the process engineer to investigate shifting the averageprocess output (X-BAR) closer to the engineering design target(P-target) for the predictor part characteristic.

There is also great utility in comparing the average process output(X-BAR) for the predictor characteristic to its production target(P-target*). The following situations are possible, assuming that theprocess output distribution is symmetrical and the predictorcharacteristic target value (P-target*) is set at the midpoint of itsmaximum allowable range:

The closer the average process output (X-BAR) for the predictorcharacteristic is to the predictor production target (P-target*), thegreater the likelihood that the process will produce parts that are incompliance.

When the average process output (X-BAR) is equal to the predictorproduction target (P-target*), the chances are maximized that theprocess will produce parts that are in compliance.

Similar conclusions can be reached even if the process outputdistribution is not symmetrical. In this situation, P-target* should beset at the point where the tails of the distribution have equal areasoutside of P-range*.

Thus, the present invention facilitates determining the differencebetween the average process output (X-BAR) and the predictor productiontarget (P-target*). This difference establishes both the magnitude andthe direction that the average process output (X-BAR) should be shifted.With this knowledge, it is then possible to adjust one or more processcontrol settings to move the average process output along the regressionline to or closer to the predictor production target (P-target*).

The present invention provides further utility. It now becomes possibleto determine whether the actual process variability (VAR) is too largerelative to the maximum allowable range (P-range*). If this is the case,then a first option is to reduce the process variation. A second optionis to increase the magnitude of the design tolerances. A third option isto do some combination of the previous two alternatives. The presentinvention can greatly facilitate efficiency and cost savings byrequiring that the various process capability analyses discussed in thissection be performed only one time for only the predictor characteristicinstead of the 30 or 40, or however many total part characteristics,involved.

Moreover, the Constraint Table values provide other useful informationfor the design engineer. For example, if the decision is made toincrease the size of the design tolerances, the Constraint Tablefacilitates a prioritized determination to be made as to 1.) whichspecification limit (upper or lower) for 2.) which articlecharacteristic is most constraining and should be the first to berelaxed. This step can be repeated as often as desired, working“outward” from the most constraining to the least constraining partcharacteristic. The design engineer can also assess the impact ofrelaxing each tolerance on product performance and factor thisinformation into the decision-making process.

The present invention creates still more utility. The design engineernow has information that enables a study to be conducted that evaluatesthe tradeoff between product performance and producibility. In addition,the design engineer may also, if circumstances permit, change the designtarget to the determined predictor characteristic target (P-target*),and make changes elsewhere in the system to compensate (if compensationis even required, for the change in the design target).

To condense the next series of comments, P-range*, VAR, and TOL (thedifference between the upper and lower specification limits), will berepresented by A, B, and C. In a similar fashion, P-target*, X-BAR andP-target will be represented by X, Y, and Z. The present inventionfacilitates the following comparisons:

-   -   1. A versus B;    -   2. A versus C;    -   3. B versus C;    -   4. X versus Y;    -   5. X versus Z; and,    -   6. Y versus Z.        As previously noted, there is exceptionally valuable information        that can be gained from these comparisons.        D. Application Overview and Summary

FIG. 21 summarizes the concepts discussed above and illustrates a methodaccording to an embodiment of the present invention. For didacticpurposes, an injection molding process is described. As FIG. 21 shows,the design of a part, for example, yields various design targets andspecification limits for the article characteristics (602), which yieldsthe design and fabrication of a mold including at least one cavitydefining a part (604). Other inputs to the process include noisevariables (606) and process control settings (607). The process (208)yields either experimental output (610) or production output (630), asdiscussed below.

As FIG. 21 shows, embodiments of the present invention can be used tofacilitate the design and engineering processes associated withdesigning a part and/or engineering a process that will ultimately yieldacceptable parts for production output. As discussed above, in oneembodiment, a process operator generates a set of parts having a rangeof variation as to a plurality of article characteristics (experimentaloutput 610). The article characteristics associated with theexperimental output 610, or a sample thereof, are assessed and analyzedusing the correlation and regression analysis methods discussed above(612). With the information gleaned from these analysis methods, theuser, aided by the Constraint and/or Relaxation Tables discussed above,may decide to change tolerance limits (618) and/or design targets (620).In addition, the user, aided by the information set forth in the OffsetTable discussed above, may decide to change process inputs (616) and/oradjust control variables (614).

As discussed above, in one embodiment, variation is induced in the partcharacteristics during an experimental production run. One of theby-products of that experimental production run is that the user learnswhich process settings have a major impact on the part characteristics.That knowledge enables the user to adjust a small number of processsettings to position product output at any predetermined point along theregression model. In the case of injection molding, for example, theuser may find that changing just one pressure setting, or onetemperature setting, or one speed setting will be sufficient totranslate the joint output of the article characteristics along theregression line.

Furthermore, the user may select a predictor characteristic (636) basedon the correlation and regression analysis (612) to facilitatemeasurement of production output (630). For example, by analyzing theremaining article characteristics in relation to the predictorcharacteristic the user may identify robust predicted characteristics(i.e., article characteristics that will always be within tolerancelimits) and eliminate them from measurement (step 634). Alternatively,or in conjunction therewith, the user may determine the maximumallowable range for the predictor characteristic and determine whetherproduction output 630 complies with specification limits by measuring asingle article characteristic (the predictor characteristic) (632).

As discussed above, to shift output along the regression line, aninjection mold operator can change one or more process controlvariables, such as pressure, temperature, speed, etc. For illustrativepurposes, an example of changing process inputs in the injection moldingindustry would be to change an internal dimension of a mold cavity. Theregression line can be shifted vertically. In FIG. 6, this would beaccomplished by changing the size of the remaining articlecharacteristic. A reduction in size would be accomplished by addingmaterial to the inside of the mold at the location of that articlecharacteristic. This would decrease the size of the produced article andwould shift the regression line vertically downwards. The size of therequired shift would be computed by determining the distance theregression line was offset from the target intersection. Thus, thelocation of the regression line relative to the target intersectionprovides information that can be used to determine which direction theregression line needs to be shifted and the magnitude of that shift.

An alternate method of shifting the regression line in FIG. 6 would beto shift it horizontally. In order for the regression line to passthrough the target intersection in FIG. 6, it must be shifted to theright. This shift would be accomplished by changing the mold dimensionfor the predictor characteristic. A shift to the right means that thesize of the predictor characteristic is increased. An increase in sizerequires an increase in the size of the mold cavity for the predictorcharacteristic (here, a dimension). This can be accomplished by removingmaterial from the interior of the mold. The size of the required shiftis determined by computing the horizontal distance between theregression line and the target intersection.

Yet another method of shifting the regression line is to create theshift by changing some combination of mold dimensions for both thepredictor and at least one of the remaining article characteristics. Inthe specific example shown in FIG. 6, the regression line is shifted ina direction perpendicular to itself. This, in effect, is the shortestpossible shift that can be done to position the regression line throughthe target intersection. In this case, the shift would be accomplishedby a decrease in the size of the predicted characteristic and anincrease in the size of the predictor characteristic. In the case of aplated part, an example of the two article characteristics could be thepost-plating length and width dimensions of the part. In this case, thepre-plating length and width dimensions of the part would be consideredas the process inputs.

FIG. 7 illustrates a method used to produce articles that havecharacteristics that are superimposed on or congruent with the targetintersection. The embodiment illustrated in FIG. 7 consists of atwo-step process. In step one, the regression line is shifted so that itintersects the target intersection. In this example, the regression lineis shifted down and to the right. As indicated before, the regressionline can be shifted horizontally, vertically, or both. In step two, thecharacteristic position is shifted, for this particular example, alongthe regression line in the direction of smaller dimensions until theposition is congruent with the target intersection. In practice, ofcourse, the direction that the characteristic position will need to beshifted will depend on the location of the initial characteristicrelative to the target intersection and the slope of the regressionline.

D.1 Simulation Mode

The previous description of process analysis system 100 describedprocess analysis system 100 operating in an analytical mode. Processanalysis system 100, in one embodiment, also operates in a simulationmode that simulates the operation of the manufacturing system used toproduce the articles being analyzed and the measurement system used tomeasure the article characteristics, including the manufacturingprocess, the measurement process, tooling, pre-process dimensions suchas molds, tooling, fixtures, software and CNC programming, materialresponses and natural variation.

In one embodiment, process analysis system 100 includes a processsimulation module 200 that allows the user to simulate the effect ofcontemplated changes—(i.) to engineering design targets, (ii.) toengineering design tolerances, (iii.) to pre-process dimensions and(iv.) to the measurement system (i.) without changing design targets,(ii.) without changing design tolerances, (iii.) without modifyingtooling, (iv.) without changing pre-process dimensions, (v.) withoutproducing new parts, (vi.) without measuring article characteristics onnew parts and (vii.) without changing the measurement system. Thesimulation module 200 of process analysis system 100 enables the user toverify whether or not the contemplated changes to design targets, designtolerances, pre-process dimensions and the measurement system will havethe desired effect without incurring the time and expense involved inactually making the changes, producing parts, measuring partcharacteristics and then determining whether the changes accomplishedthe desired objectives.

As previously discussed, process analysis system 100 analyzes a data setof article characteristic values and allows the user to assess, amongother things, the producibility of a given article at design targetvalues and/or within design tolerances, as well as what changes could bemade to increase the producibility of the article, such as changingpre-process dimensions and/or increasing the operating range throughtolerance relaxations and/or bringing the output closer to targetspecifications and/or reducing variability. For example, as previouslydiscussed, process analysis system 100 provides information such asOffset Tables detailing the amount by which a given articlecharacteristic is offset from design target values. The tolerancerelaxation tables allow the user to assess the effect of increasingtolerances on the operating range of the process. As discussed below,simulation module 200 of process analysis system 100, in one embodiment,allows the user to simulate changes to the design and to themanufacturing process, allowing the user to assess whether thecontemplated changes achieved desired objectives (such as increasing theoperating range or increasing quality by producing closer to designtargets).

FIG. 39 illustrates the overall process flow associated with anembodiment of the present invention. In one embodiment of simulationmodule 200, the user inputs (i.) correction factors for pre-processdimensions, and/or (ii.) revised values for design targets, and/or(iii.) revised values for design tolerances; and/or (iv.) revised valuesof the accuracy of the measurement system. As discussed above, processanalysis system 100 receives and analyzes a data set of articlecharacteristic values relative to design target values and designtolerances (910). The user assesses the analysis output and decides onone or more modification parameters, such as changes to pre-processdimensions and/or design specifications (such as design targets ortolerances). Simulation module 200 of process analysis system 100receives the modification parameters (912) and creates a new data set ofarticle characteristics simulating the results of the revisedmanufacturing process and/or revised design targets and/or reviseddesign tolerances and/or revised measurement system (914). Once the userhas input the correction factors and/or revised engineering designvalues, process analysis system 100 simulates the manufacturing processoperating with modified and/or corrected pre-process dimensions.Simulation module 200 of process analysis system 100, in one embodiment,creates a new set of part dimensional data. This new set of dimensionaldata along with revised(if any) engineering design targets and/ortolerances is then analyzed using the analysis functionality of processanalysis system 100, as discussed above, to verify that the desiredresults were obtained from the changes (916).

In one implementation, changes to the measurement system used to measurethe article characteristic values can be implemented by increasing ordecreasing the distance of the upper and lower prediction intervals fromthe regression model by some amount or percentage specified by the user.The user may then observe the changes to the operating range, theoperating target, the Tolerance Relaxation tables and the like resultingfrom changing the magnitude of the prediction intervals. In oneembodiment, the user can simulate increases in measurement systemaccuracy by decreasing the magnitude of the prediction intervals.Similarly, the user can simulate decreases in measurement systemaccuracy by increasing the magnitude of the prediction intervals.

In a second implementation, the user can change the magnitude of theprediction intervals even though there is no change to the accuracy ofthe measurement system. In this implementation, changes to the magnitudeof the prediction intervals will include a greater or lesser percentageof data points. In-other-words, changing the size of the predictionintervals will change the percentage of the distribution encompassedwithin the prediction intervals. This is also referred to as changingthe confidence interval.

FIG. 38 illustrates a Simulated Offset Table providing 1) the previouslycomputed analytical offsets resulting from the original, unmodifiedarticle characteristic data set; 2) the simulated offset value; and 3)the correction factor specified by the user as to each desired articlecharacteristic. The didactic example displayed in FIG. 38, illustrates asimulation where the user has elected to modify only those tooling (orpre-process) dimensions where the absolute value of the analyticaloffset is equal to or greater than 0.003″. In this example, the user hasdecided that the simulated modifications to the pre-process dimensionsare equal to the desired modifications (analytical offsets) computed byprocess analysis system 100. Of course, the user can implement anydecision rule the user wants to determine the correction factor. Theuser could decide, for example, to correct for all offsets, irrespectiveof their magnitude or direction. The user also has the option ofcreating a correction factor for the predictor dimension. As previouslydiscussed, in one embodiment the analytical offset is the distance theregression line is above (a positive offset) or below (a negativeoffset) the target intersection.

As discussed above, these simulated changes to the design, thepre-process dimensions and the measurement system are used to create asimulated set of data representing what the manufacturing process outputwould be using the revised parameters. This simulated data set is thenprovided by simulation module 200 as input to process analysis system100. Process analysis system 100 then analyzes the revised data set. Theresults from process analysis system 100 using the revised data setprovide a prediction of the part characteristics that will be generatedby the manufacturing process using revised tooling and/or pre-processdimensions. If the examples displayed in FIGS. 28B and 38 represent aninjection molding process, the analytical offset is the amount each molddimension is greater than (a positive value) or less than (a negativevalue) the ideal dimension. The ideal pre-process dimension will resultin a regression line that passes through the target intersection. If ananalytical offset is positive, the predicted dimension is too large andthe correction factor in FIG. 38 is negative. A negative correctionfactor is the amount that mold dimension should be reduced. In practice,one method for reducing the size of a mold dimension is by adding steel(or other material) to the mold by welding and then machining thatdimension to the desired value. If an analytical offset is negative, thepredicted dimension is too small and the correction factor in FIG. 38 ispositive. A positive correction factor is the amount that mold dimensionshould be increased. In practice, one method for increasing the size ofa mold dimension is by removing steel (or other material) from the moldby machining it away. One skilled in the art will be able to applycorrection factors to pre-process dimensions for manufacturing processesother than injection molding. For some processes, the correction factorwill apply to mold or other tooling or software dimensions. For otherprocesses, such as a plated part, the correction factor can apply to apre-process (pre-plating) part dimension. In addition, the simulationcan also extend to other analytical functionalities associated withprocess analysis system 100. For example, the modified data set,including modified design targets and tolerances can be used to generatea simulated constraint table and/or design tolerance relaxation table.

FIGS. 46A thru 46D illustrate the simulation of a given process relativeto a first article characteristic (P) and a second articlecharacteristic (C), as well as the various modification parameters whichmay be specified and simulated. For clarity, the upper and lowerprediction intervals are omitted from FIGS. 46A thru 46D. FIG. 46A showsa regression model between a first article characteristic (P) and asecond article characteristic (C), as well as the target intersectionand upper and lower specification limits for the articlecharacteristics. In this example, the regression line is above the upperspecification limit of the second article characteristic andnon-conforming parts will be produced. As discussed above, a user maywish to modify one or more design target values, design tolerances,and/or a pre-process parameter in order to produce conforming partsand/or increase producibility and/or to meet other objectives. Theprocess simulation module 200 then simulates the effect of thechange(s), essentially for all possible combinations of process controlsettings. FIG. 46B illustrates a simulation of the process modeled inFIG. 46A with a modification to an upper specification limit for thesecond article characteristic (C). The region of conformance isincreased in area so that it now encompasses the regression line (andthe upper and lower prediction intervals) and conforming parts will beproduced. FIG. 46C illustrates a simulation of a process for which thedesign target for the second article characteristic (C) has beenmodified. In this example, the area of the region of conformance remainsunchanged. However, the region of conformance is shifted so that itencompasses the regression line (and the upper and lower predictionintervals) and conforming parts will be produced. FIG. 46D illustrates asimulation of a process for which a pre-process parameter, such as thedimension of tooling used in the process, corresponding to the secondarticle characteristic (C) has been modified. In this example, the areaand location of the region of conformance remains unchanged. However,the regression line is shifted so that it passes through the targetintersection. The regression line, however, does not have to be shiftedso that it passes through the target intersection. One could decide, forexample, to shift the regression line only to the extent that theregression line and the upper and lower prediction intervals are withinthe region of conformance.

The following provides an outline of the overall process flow associatedwith use of the process analysis system to at first analyze an originalarticle characteristic data set, and then to run a simulation based onmodifications to either tooling and/or pre-process dimensions and/ordesign targets and/or design tolerances and/or measurement systems.

DIDACTIC EXAMPLE OF A SIMULATION

1. Display the analytical offsets from the target values (FIG. 38, forexample).

2. Display the Pmin Tolerance Relaxation Table (FIG. 30B, for example).

3. Display the Pmax Tolerance Relaxation Table (FIG. 30C, for example).

4. Display the scatter plots (FIG. 40, for example).

5. Display the original design targets and design tolerances (FIG. 41,for example).

6. The user determines the correction factor for each partcharacteristic which affects, for each part characteristic, thepre-process dimension (FIG. 38, for example).

7. The user determines changes to design targets.

8. The user determines changes to design tolerances.

9. The user determines changes to the accuracy of the measurementsystem.

10. Simulation module 200 of process analysis system 100 receivesmodification parameters, including the correction factors forpre-process dimensions, changes to design targets changes to designtolerances

11. Simulation module 200 simulates the operation of the manufacturing(and measurement) process by applying the correction factors to theoriginal article characteristic data set and creating a new articlecharacteristic data set.

12. Simulation module 200 creates a new data set. The new data set isthe new data input for process analysis system 100. The new data setcontains the simulated article characteristic data set, the new designtargets, and the new design tolerances, which incorporate any changesthe user made.

13. Process analysis system 100 analyzes the new data set.

14. Process analysis system 100 creates a new set of output data.

15. Steps 1.-13. are repeated until the user is satisfied with theresults.

16. Once satisfied, the user implements the desired changes to (i.)design targets, (ii.) design tolerances, (iii.) pre-process dimensions,and/or (iv.) the measurement system.

D.2. Analytical Simulation

In the implementation described above, the simulation relative topre-process parameters, such as pre-process dimensions of tooling, etc.,is accomplished by modifying the data set of article characteristicvalues according to the modification parameters specified by a user, andrunning the modified data set through the process analysis system 100described above. In other implementations, however, this simulation canbe done analytically by changing the coefficients, or other aspects, ofthe equation(s) that describe the regression models between articlecharacteristics. Figure XD illustrates a linear regression model, whichcan be represented by the equationY=mX+b,where:

-   -   m equals the slope of the regression line;    -   b is the value of the y-intercept; and,    -   r is the value of the x-intercept (see FIG. 47).

In one implementation, process analysis system 100 computes the slope(m) and the y-intercept (b) in order to render the correlation andregression diagrams on a display. Accordingly, changes to they-intercept value (b) shifts the regression model vertically. FIG. 45illustrates a linear regression model between a predictor characteristic(P) and a remaining article characteristic (C) and the vertical,horizontal and perpendicular offsets between the regression model andthe target intersection where:

-   -   TI is the target intersection;    -   P_(T) is the target value of P;    -   C_(T) is the target value of C;    -   C_(RXPT) is the value of C at the point on the regression line        where P is equal to P_(T);    -   P_(RXCT) is the value of P at the point on the regression line        where C is equal to C_(T);    -   VO is the vertical offset or vertical distance between the        regression line and the target intersection (C_(RXPT)-C_(T))    -   HO is the horizontal offset or horizontal distance between the        regression line and the target intersection (P_(RXCT)-P_(T));        and,    -   PO is perpendicular offset between the regression line and the        target intersection.

When the regression line, evaluated at the target value for thepredictor characteristic, is above the target intersection, the offsetis positive. When the regression line, evaluated at the target value forthe predictor characteristic, is below the target intersection theoffset is negative. The magnitude and direction (positive or negativesign) of the offset can be obtained from the Offset Table (see above).FIG. 45 also graphically illustrates the vertical, horizontal andperpendicular offsets.

Subtracting the vertical offset (VO) from the y-intercept (b) of theequation defining the regression line moves the regression line in adirection closer to the target intersection. If the vertical offset ispositive, subtracting a positive number from the y-intercept decreasesthe value of the y-intercept (b). The regression line shifts downwardscloser to the target intersection (TI). Similarly, the regression lineshift downwards (closer to the target intersection) when the positiveoffset is subtracted from the data set of article characteristic values.

FIG. 45 illustrates three of many offsets that can be implemented. Asdiscussed herein, there are several ways of computing the offset. Theoffset can be a vertical offset, a horizontal offset, or a perpendicularoffset (PO) (see FIG. 45), or any other distance measured relative tothe target intersection. All offsets can be determined graphically oranalytically. The horizontal offset is the distance between the value ofthe predictor at the point at which the regression line crosses thetarget value of the predicted dimension less the target value of thepredictor. The regression line can be shifted horizontally by changingthe value of the x-intercept of the regression line (r). When thehorizontal offset is positive, the regression line is to the right ofthe target intersection. In this case, the horizontal offset needs to besubtracted from the data set or from the x-intercept to move theregression line to the left.

For didactic purposes, the language contained in this application refersto the use of techniques to “locate” or “position” or “determine theintersection” or “determine the range” or other terminology that mightbe used from a graphical perspective. Virtually all analyticaltechniques described herein document can be accomplished eithergraphically or analytically. It is to be appreciated that analyticaltechniques can be used when graphical techniques are described andgraphical techniques can be used when analytical techniques aredescribed. Indeed, a preferred embodiment of the present inventionperforms all computations, calculations, locations and determinationsusing analytical techniques. Graphical displays are created for theconvenience and understanding of the user.

Also for didactic purposes, the language in this application refers to aregression line. It is noted above that the regression “line” need notbe a straight line but may be curvilinear. It should also be noted thatthe regression model is frequently shown, for didactic purposes, asbeing a single line. It should be noted the regression model in apreferred embodiment of this invention includes use of predictionintervals.

It is to be noted, that for didactic purposes, the effect of changes inprocess control settings has been illustrated in terms of their effecton a single remaining (predicted) article characteristic. It is to beunderstood that the effect of changes in process control settings can bedetermined for more than two article characteristics. Similarly, it isto be understood that the effect of changes in process inputs has beenillustrated in terms of their effect on a single regression model. It isto be understood that the effect of changes in process control settingscan be determined for more than one regression model.

For didactic purposes, it has been assumed that the objective ofintroducing changes in either the process control settings and/orprocess inputs has been to move the joint operating position(s) and/orregression model(s) closer to one or more target intersections. It is tobe further understood that these changes can be made to move the jointoperating position(s) and/or regression model(s) to any desiredposition.

For didactic purposes, the upper and lower specification limitsassociated with the article characteristics have been described as beingconstant values, resulting in a rectangular compliance area. The presentinvention, however, can also be applied to circumstances where one ormore of such specification limits vary based on one or more factors,resulting in a compliance area having a trapezoidal (for example) orother shape.

Finally, for didactic purposes, as noted immediately above, it has beenassumed that changes have been introduced to optimize articlecharacteristics relative to one or more criteria. It is to be understoodthat the algorithms, models and concepts set forth herein can be used toachieve the opposite effect. For example, it is possible to determinethe required change in a joint operating point and/or regression modelposition needed in order to achieve a desired change in a processsetting and/or process input. One objective of doing this could be tomove a process control setting away from a dangerous or harmful setting.Another objective of doing this could be to match the production processand/or engineering design parameters to specific process inputs such aspre-configured raw material shapes.

Lastly, although the present invention has been described as operatingin connection with injection molding processes, the present invention,as discussed above, has application to a variety of processes. Forexample, the present invention has application to sheet metal forming,plating and semiconductor manufacturing, as well as any other processwhere a material is added, removed, or changed in form or structure. Thepresent invention has application to other non-manufacturing processeswhere characteristics of the output are related. Accordingly, thepresent invention has been described with reference to specificembodiments. Other embodiments of the present invention will be apparentto one of ordinary skill in the art. It is, therefore, intended that thescope of the invention not be limited to the embodiments describedabove.

1. A method facilitating analysis of a process directed to the creationof a set of articles, wherein the set of articles exhibit a range ofvariation as to a plurality of article characteristics, the methodcomprising identifying a model of the process, wherein the modelcharacterizes the regression relationship between at least two articlecharacteristics in the plurality of article characteristics; receivingat least one modification parameter; and changing the model of theprocess based on the at least one modification parameter.
 2. The methodof claim 1 wherein the model comprises a regression model between afirst article characteristic and at least a second articlecharacteristic.
 3. The method of claim 2 wherein the regression model islinear.
 4. The method of claim 2 wherein the regression model isnon-linear.
 5. The method of claim 1 wherein the model further includesat least one design specification value corresponding to an articlecharacteristic in the plurality of article characteristics.
 6. Themethod of claim 5 wherein the at least one design specification value isa target value.
 7. The method of claim 5 wherein the at least one designspecification value is a design tolerance.
 8. The method of claim 5further comprising displaying a graphical representation of the modelrelative to the at least one design specification value.
 9. The methodof claim 1 wherein the model further includes a predictor characteristicselected from the plurality of article characteristics.
 10. The methodof claim 1 wherein the process includes at least one pre-processparameter; and wherein at least one modification parameter characterizesa change to a pre-process parameter.
 11. The method of claim 1 whereinat least one of the article characteristic values is measured using ameasurement system, and wherein the modification parameter characterizesa change to the measurement system.
 12. The method of claim 11 whereinthe model of the process includes upper and lower prediction intervals;and wherein changing the model of the process comprises narrowing thespacing between the upper and lower prediction intervals.
 13. The methodof claim 5 wherein the modification parameter characterizes a change toa design specification value.
 14. The method of claim 6 wherein themodification parameter characterizes a change to the design target. 15.The method of claim 7 wherein the modification parameter characterizes achange to the design tolerance.
 16. A method facilitating analysis of aprocess directed to the creation of a set of articles, wherein the setof articles exhibit a range of variation as to a plurality of articlecharacteristics, the method comprising identifying a model of theprocess, wherein the model includes a regression model between a firstarticle characteristic and a second remaining article characteristic inthe plurality of article characteristics, and wherein the model containsone or more instances of any one of the following: a design targetcorresponding to an article characteristic, a lower specification limitfor an article characteristic, an upper specification limit for anarticle characteristic; receiving at least one modification parameter;and changing the model based on the at least one modification parameter.17. The method of claim 16 further comprising displaying a graphicalrepresentation of the model.
 18. The method of claim 16 wherein theprocess includes at least one pre-process parameter, and wherein the atleast one modification parameter characterizes a change to a pre-processparameter.
 19. The method of claim 16 wherein the at least onemodification parameter characterizes a change to a design target. 20.The method of claim 16 wherein the at least one modification parametercharacterizes a change to a lower specification limit.
 21. The method ofclaim 16 wherein the at least one modification parameter characterizes achange to an upper specification limit.
 22. The method of claim 16wherein the article characteristic values are obtained from ameasurement system, and wherein the modification parameter characterizesa change to the measurement system.
 23. The method of claim 18 whereinthe pre-process parameter is a pre-process dimension.
 24. The method ofclaim 23 wherein the pre-process dimension characterizes a dimension oftooling used in the process.
 25. The method of claim 23 wherein thepre-process dimension characterizes a dimension of a material objectmodified by the process.
 26. A method facilitating analysis of a processdirected to the creation of a set of articles, wherein the set ofarticles exhibit a range of variation as to a plurality of articlecharacteristics, the method comprising identifying a model of theprocess including a regression model(s) between a predictorcharacteristic selected from the plurality of article characteristicsand at least one remaining article characteristic in the plurality ofarticle characteristics, receiving at least one modification parameter;and changing the model of the process based on the at least onemodification parameter.
 27. The method of claim 26 wherein the model ofthe process contains one or more instances of any one of the following:a design target corresponding to an article characteristic, a lowerspecification limit for an article characteristic, an upperspecification limit for an article characteristic, and a pre-processparameter.
 28. The method of claim 26 further comprising receiving adesign specification value for the predictor characteristic and a designspecification value for said at least one of the remaining articlecharacteristics; and wherein the design specification value for thepredictor characteristic and the design specification value for said atleast one of the remaining article characteristics are located relativeto the regression model(s) between the predictor characteristic and saidat least one remaining article characteristics.
 29. The method of claim26 further comprising displaying a graphical representation of themodel.
 30. The method of claim 27 wherein the at least one modificationparameter characterizes a change to a pre-process parameter.
 31. Themethod of claim 27 wherein the at least one modification parametercharacterizes a change to a design target.
 32. The method of claim 27wherein the at least one modification parameter characterizes a changeto a lower specification limit.
 33. The method of claim 27 wherein theat least one modification parameter characterizes a change to an upperspecification limit.
 34. The method of claim 27 wherein the articlecharacteristic values are obtained from a measurement system, andwherein the modification parameter characterizes a change to themeasurement system.
 35. The method of claim 30 wherein the pre-processparameter is a pre-process dimension.
 36. The method of claim 35 whereinthe pre-process dimension characterizes a dimension of tooling used inthe process.
 37. The method of claim 35 wherein the pre-processdimension characterizes a dimension of a material item modified by theprocess.